LangChain is a versatile and comprehensive framework designed for constructing applications around large language models (LLMs). It offers a structured approach to development by chaining together various components essential to language model applications. Moreover, these components include prompt templates, LLMs themselves, and agents that act as the interface between users and the language model.

Curious? Keep reading; you won’t believe what’s possible with LangChain! Don’t worry if you have never heard of it before– this article will walk you through the very basics.

A Framework for Building Language Models

At its core, LangChain provides a framework that simplifies the complex process of building, managing, and scaling applications that utilize language models. Unlike traditional development workflows where one has to handle the various moving parts of a language model application individually, LangChain offers an efficient and standardized way of managing these components.

Chains Together Different Components

Prompt Templates

These are pre-formulated prompts that can be used to instruct language models more effectively. Instead of coming up with a new prompt every time, developers can use reusable templates that help in eliciting more accurate and useful responses from the language models.

Large Language Models (LLMs)

LangChain is compatible with various large language models, such as GPT 4, LLaMA 2, PaLM, etc., and makes it easier to integrate them into applications. This eliminates the hassle of dealing with proprietary APIs or complex configurations.

Agents

These are the intermediaries between the users and the language model. Addtionally, they handle tasks like user input validation, data pre-processing, and routing the information to and from the language model.

Benefits of Using LangChain

LangChain offers a robust and streamlined approach to integrating language models into various applications. Its user-friendly and modular design addresses many challenges faced by developers, offering key advantages such as:

• Flexibility: Modular components make customization straightforward.
• Scalability: Built to grow with your project’s needs.
• Streamlined Development: Quick and efficient from ideation to deployment.

And that’s not all! It also solves the following problems:

• Complexity: Managing and deploying LLMs can be complex and time-consuming. And LangChain abstracts away this complexity, making it easy to use LLMs in your projects.
• Cost: LLMs can be expensive to train and deploy. LangChain provides a way to share LLMs, so you don’t have to train or deploy your own.
• Accuracy: LLMs can be very accurate, but they can also be biased. LangChain provides a way to mitigate bias in LLMs, so you can be confident in the results they produce.

Great, but how do I use this LangChain?

How to Get Started with LangChain

Getting started with LangChain is an easy process designed to be user-friendly. Hence, follow the steps below to quickly set up your environment and dive into building powerful applications with language models.

Requirements

Before getting started with LangChain, ensure you have the following prerequisites in place:

Software

• Python 3.6 or Higher: LangChain requires Python 3.6 or above. You can download the latest Python version from the official website.

Libraries

• OpenAI Python Package (Optional): If you plan on using OpenAI’s GPT models, you will need their Python package. This will be installed in the installation steps.

Accounts & API Keys

• OpenAI Account (Optional): If you plan to use OpenAI’s GPT models, you’ll need an OpenAI account to obtain an API key. Sign up here.
• ColossalAI Account (Optional): If you’re using ColossalAI, you’ll need to register and obtain an API key.

Hardware

• Memory & CPU: While LangChain is designed to be lightweight, the language models it interacts with can be resource-intensive.

Installation

1. Install LangChain: Open your terminal and run the following command to install LangChain: pip install langchain
2. Install Dependencies: If you plan on using OpenAI’s model APIs, install the necessary Python package: pip install openai

Environment Setup

• API Key Configuration: You’ll need to acquire an API key from the language model provider. For OpenAI, create an account and get the API key. After that, set it as an environment variable like so:

export OPENAI_API_KEY="your_openai_api_key_here"

Replace the string with your OpenAI key above. Now we can get started with the real development process!

Basic Usage

• Initialize Language Model: You can use various language models with LangChain. For this example, we will use ChatGPT by OpenAI and ColossalAI as our LLM (Large Language Model).

• Initialize ChatGPT:

from langchain.llms import OpenAI 
chatgpt_llm = OpenAI(api_key=os.environ["OPENAI_API_KEY"], model="gpt-4.0-turbo")

• Initialize ColossalAI:

from langchain.llms import ColossalAI colossal_llm = ColossalAI(api_key="your_colossal_api_key_here")

• Create a Chain: LangChain allows you to create a chain consisting of an LLM, prompt templates, and agents to perform specific tasks. Here’s a simple example that uses a chain to answer questions.

from langchain import LLMChain, PromptTemplate, Agent

# Create a PromptTemplate for question answering
question_template = PromptTemplate("answer the question: {question}")

# Create an Agent to handle the logic
qa_agent = Agent(prompt_template=question_template, llm=llm)

# Create a chain
chain = LLMChain(agents=[qa_agent])

# Use the chain
response = chain.execute({"question": "What is the capital of France?"})
print(response)

This should print output like {'answer': 'The capital of France is Paris.'}

Not so hard, right? Next we focus on more specific prompts.

Create Prompt Templates and Agents

Now let’s create two specific prompt templates and agents for the chatbot functionality for ChatGPT and ColossalAI.

1. Question Answering: Creating prompt template for Q/A.

question_template = PromptTemplate("Answer this question: {question}") 
qa_agent = Agent(prompt_template=question_template, llm=chatgpt_llm)

2. Small Talk: Creating prompt template for small talk.

small_talk_template = PromptTemplate("Engage in small talk: {text}") 
small_talk_agent = Agent(prompt_template=small_talk_template, llm=colossal_llm)

Then, we must get everything connected.

Chaining It All Together

Here we create a chain that consists of multiple agents to handle different tasks.

from langchain import LLMChain

chain = LLMChain(agents=[qa_agent, small_talk_agent])

# For question answering
qa_response = chain.execute({"question": "What is the capital of France?"})
print(qa_response)  # Output: {'answer': 'The capital of France is Paris.'}

# For small talk
small_talk_response = chain.execute({"text": "How's the weather?"})
print(small_talk_response)  # Output: {'response': 'The weather is lovely! How can I assist you further?'}

What if you want to change the language model you use for an agent? It’s simple and the next section discusses how to do that.

Switching Language Models

You can easily switch between different language models like ChatGPT and ColossalAI by changing the llm parameter when initializing the agent.

# Switching to ColossalAI instead of ChatGPT for question answering
qa_agent = Agent(prompt_template=question_template, llm=colossal_llm)

# Use the chain again
qa_response = chain.execute({"question": "What is the capital of Japan?"})
print(qa_response)  # Output should differ depending on the model.

What we’ve seen so far is merely the tip of the iceberg! Don’t scratch your head and keep reading to know how we can enhance the functionalities further!

Expanding LangChain Functionality with Additional Agents

LangChain allows for extra complexity by letting you include more than just question-answering and small talk in your chatbot.

Initialize Additional Agents

Below, we illustrate how to expand your existing chatbot setup to also handle tasks like sentiment analysis and language translation.

1. Sentiment Analysis

entiment_template = PromptTemplate("Analyze sentiment: {text}")
sentiment_agent = Agent(prompt_template=sentiment_template, llm=chatgpt_llm)

2. Language Translation (English to Spanish)

translation_template = PromptTemplate("Translate from English to Spanish: {text}")
translation_agent = Agent(prompt_template=translation_template, llm=colossal_llm)

Extend Your Existing Chain

Then, add these new agents to your existing chain.

chain = LLMChain(agents=[qa_agent, small_talk_agent, sentiment_agent, translation_agent])

Execute The New Chain

1. Sentiment Analysis

sentiment_response = chain.execute({"text": "I am so happy today!"}) 
print(sentiment_response) 
# Output: {'sentiment': 'positive'}

2. Language Translation (English to Spanish)

translation_response = chain.execute({"text": "Hello, how are you?"}) 
print(translation_response) 
# Output: {'translation': 'Hola, ¿cómo estás?'}

Combining Multiple Agents for a More Robust Chatbot

Here’s how you can combine different functionalities to create a more versatile chatbot that reacts to the sentiment of a user:

user_input = "Tell me a joke!"
small_talk_response = chain.execute({"text": user_input})

joke = small_talk_response['response']
sentiment_response = chain.execute({"text": joke})
user_sentiment = sentiment_response['sentiment']

if user_sentiment == 'positive':
    print(f"Chatbot: {joke}")
else:
    print("Chatbot: I apologize for the earlier joke. How can I assist you further?")

More Programming Use Cases

Langchain can also assist you in coding more efficiently and easily.

SQL Database Operations

For instance, you can even write an agent to perform SQL queries and return the result:

sql_query_template = PromptTemplate("Execute SQL Query: SELECT * FROM {table}")
sql_query_agent = Agent(prompt_template=sql_query_template, llm=chatgpt_llm)

Then, to execute this agent, add it to your chain and execute it:

chain = LLMChain(agents=[qa_agent, small_talk_agent, sql_query_agent])
sql_response = chain.execute({"table": "users"})
print(sql_response)  
# Output: {'result': [...]}

Code Writing

LangChain can dynamically write code snippets for you:

code_template = PromptTemplate("Write Python code to: {task}")
code_agent = Agent(prompt_template=code_template, llm=colossal_llm)

For example, to generate code for a simple “Hello, World!” application:

chain = LLMChain(agents=[qa_agent, small_talk_agent, code_agent])
code_response = chain.execute({"task": "print Hello, World!"})
print(code_response)  # Output: {'code': 'print("Hello, World!")'}

Pretty cool, right? Wait till you find out you can even combine its SQL and code writing capabilities!

Combining SQL and Code Writing

Imagine you want to generate a Python code snippet that performs a SQL query. You can achieve this by chaining the agents:

code_sql_response = chain.execute({"task": "perform SQL query", "table": "users"})
print(code_sql_response)  # Output: {'code': '...', 'result': [...]}

The above code is just a template since you would have to provide the database details to get an output. By combining these agents, you create a chatbot that’s not only versatile in handling textual tasks but also capable of interacting with databases and generating code on the fly.

I still have an itch for creating my agent, what do I do? Well…

Code Customization

LangChain’s architecture is designed for customization. Beyond the basic agents and LLMs, you can also create your own agents to perform highly specialized tasks. For instance, let’s create a custom agent that filters out profanity from text messages.

from langchain import Agent

class ProfanityFilterAgent(Agent):
    def process(self, data):
        text = data.get('text', '')
        clean_text = text.replace('badword', '****')  # Replace 'badword' with stars and remember to write the profanity you want to filter here
        return {'clean_text': clean_text}

# Add your custom agent to a chain
chain = LLMChain(agents=[ProfanityFilterAgent(), qa_agent])
response = chain.execute({'text': 'This is a badword example.'})
print(response)

Leveraging LangChain for Diverse Use Cases

Before we dive in, let’s set the stage: LangChain isn’t just another tool in your tech stack—it’s a game-changer. From chatbots to data analytics, we’ll explore and add onto what we have discussed in regards to how this versatile platform can be the answer to a wide array of use cases.

Chatbots

LangChain enhances chatbot functionalities by enabling advanced natural language understanding. With LangChain’s ability to structure and understand chat messages using schema definitions, you can more effectively map user input to actions, thus reducing the chances of miscommunication.

from langchain import OpenAI, ChatPromptTemplate, HumanMessagePromptTemplate

llm = OpenAI(temperature=0.2, openai_api_key=openai_api_key)

prompt = ChatPromptTemplate(
    messages=[
        HumanMessagePromptTemplate.from_template("User is asking for the availability of {product_name}.")
    ],
    input_variables=["product_name"]
)

availability_query = prompt.format_prompt(product_name="Laptop Model X")
response = llm.run(availability_query)
print("Chatbot:", response)

Question Answering

LangChain’s power extends to complex question-answering scenarios, as we touched on above, like customer support, academic tutoring, and virtual assistant technology. The platform allows for the easy inclusion of retrieval-based question answering, where it can fetch the most appropriate answer from a database or a set of documents.

LangChain simplifies the integration process, making it possible to have robust Q&A systems without complex configurations.

from langchain import OpenAI, RetrievalQA

llm = OpenAI(temperature=0, openai_api_key=openai_api_key)
qa = RetrievalQA.from_chain_type(llm=llm, chain_type="qa", retriever=some_retriever_instance)

query = "What is the capital of Germany?"
answer = qa.run(query)
print("Answer:", answer)

Summarization

In an information-heavy world, summarization becomes a useful tool to distill long articles, reports, or conversations into short, manageable readouts. LangChain allows for dynamic summarization tasks to be performed easily, offering concise summaries generated through advanced NLP algorithms. You can even fine-tune the level of summarization to suit your specific needs.

from langchain import OpenAI

llm = OpenAI(temperature=0, openai_api_key=openai_api_key)

summary_query = "Summarize the following text: ..."
summary = llm.run(summary_query)
print("Summary:", summary)

Text Generation

LangChain allows for controlled text generation through its integrated models. Whether you’re generating product descriptions, headlines, or even automated news reports, LangChain’s ability to handle structured prompts can guide the text generation in the direction you want.

from langchain import OpenAI

llm = OpenAI(temperature=0.7, openai_api_key=openai_api_key)

text_gen_query = "Generate a product description for a futuristic smartwatch."
generated_text = llm.run(text_gen_query)
print("Generated Text:", generated_text)

Creative Writing

Creative writing often requires inspiration, brainstorming, and iteration. LangChain can serve as a virtual writing assistant that suggests dialogues, scenes, or entire narrative arcs. So, its advantage over other text generation tools is its ability to understand complex, user-defined prompts and schemas, offering more targeted and contextually appropriate suggestions.

from langchain import OpenAI

llm = OpenAI(temperature=0.8, openai_api_key=openai_api_key)

creative_query = "Write a dialogue between a detective and a suspect."
creative_text = llm.run(creative_query)
print("Creative Text:", creative_text)

Data Analysis

Data analysis often involves SQL queries, data transformations, and statistical calculations. LangChain can automate these steps, transforming natural language queries into executable SQL or Pandas code. Hence, this is particularly useful for business analysts and other non-technical users, allowing them to perform complex data manipulations without the need for coding skills.

from langchain import SQLDatabase, SQLDatabaseChain

sqlite_db_path = 'data/my_data.db'
db = SQLDatabase.from_uri(f"sqlite:///{sqlite_db_path}")
db_chain = SQLDatabaseChain(llm=llm, database=db)

data_analysis_query = "Calculate the average age of users in the Users table."
data_analysis_result = db_chain.run(data_analysis_query)
print("Data Analysis Result:", data_analysis_result)

PDF Interaction

Manual extraction of specific data from PDFs can be extremely time-consuming, especially for large sets of documents. LangChain can be paired with a PDF processing library to read, extract, and even modify PDF content using natural language queries. As a result, this could be incredibly useful for professionals in law, healthcare, or academia who often need to sift through large volumes of textual data.

from langchain import OpenAI
from PyPDF2 import PdfFileReader

llm = OpenAI(temperature=0, openai_api_key=openai_api_key)

def read_pdf(file_path):
    pdf_reader = PdfFileReader(file_path)
    text = ""
    for page_num in range(pdf_reader.getNumPages()):
        page = pdf_reader.getPage(page_num)
        text += page.extract_text()
    return text

pdf_text = read_pdf('some_file.pdf')
pdf_query = f"Extract the section about financial summary from the text: {pdf_text}"
pdf_section = llm.run(pdf_query)
print("PDF Section:", pdf_section)

Deploying Your LangChain Model

After discussing its diverse use cases, let’s leverage Gradio and Streamlit’s user-friendly interfaces to deploy the LangChain models. So, whether you’re a seasoned developer or a newbie, these platforms offer code templates to expedite the process. Hence, let’s dive into how you can make your LangChain model accessible to the world in just a few simple steps.

Deployment Using Streamlit Template

Streamlit offers a straightforward way to create web apps with Python. Therefore, it can be used to deploy LangChain models.

# streamlit_app.py
import streamlit as st
from streamlit_chat import message  # Assuming you've got a widget or function to manage chat messages

from langchain.chains import ConversationChain
from langchain.llms import OpenAI

def load_chain():
    """Logic for loading the chain you want to use should go here."""
    llm = OpenAI(temperature=0)
    chain = ConversationChain(llm=llm)
    return chain

chain = load_chain()

# StreamLit UI Configurations
st.set_page_config(page_title="LangChain Demo", page_icon=":robot:")
st.header("LangChain Demo")

if "generated" not in st.session_state:
    st.session_state["generated"] = []

if "past" not in st.session_state:
    st.session_state["past"] = []

def get_text():
    input_text = st.text_input("You: ", "Hello, how are you?", key="input")
    return input_text

user_input = get_text()

if user_input:
    output = chain.run(input=user_input)
    st.session_state.past.append(user_input)
    st.session_state.generated.append(output)

if st.session_state["generated"]:
    for i in range(len(st.session_state["generated"]) - 1, -1, -1):
        message(st.session_state["generated"][i], key=str(i))
        message(st.session_state["past"][i], is_user=True, key=str(i) + "_user")

Then, to deploy, simply run:

streamlit run streamlit_app.py

Deployment Using Gradio Template

Gradio is another powerful library to turn machine learning models into web apps. It is equally effective for deploying LangChain models.

# gradio_app.py
import os
from typing import Optional, Tuple

import gradio as gr
from langchain.chains import ConversationChain
from langchain.llms import OpenAI
from threading import Lock

# Define chain and logic to load it
def load_chain():
    llm = OpenAI(temperature=0)
    chain = ConversationChain(llm=llm)
    return chain

# Set OpenAI API key
def set_openai_api_key(api_key: str):
    if api_key:
        os.environ["OPENAI_API_KEY"] = api_key
        chain = load_chain()
        os.environ["OPENAI_API_KEY"] = ""
        return chain

class ChatWrapper:
    def __init__(self):
        self.lock = Lock()
        
    def __call__(self, api_key: str, inp: str, history: Optional[Tuple[str, str]], chain: Optional[ConversationChain]):
        self.lock.acquire()
        try:
            history = history or []
            if chain is None:
                history.append((inp, "Please paste your OpenAI key to use"))
                return history, history
            import openai
            openai.api_key = api_key
            output = chain.run(input=inp)
            history.append((inp, output))
        except Exception as e:
            raise e
        finally:
            self.lock.release()
        return history, history

# Gradio UI configurations
# ... [Your Gradio UI code here]

# Launch Gradio app
block.launch(debug=True)

Challenges and Limitations of LangChain

While LangChain offers a wide array of functionalities and features, it’s important to acknowledge its challenges and limitations.

Data Bias

The Challenge

LangChain relies on machine learning models like ChatGPT and ColossalAI, which are trained on vast datasets that could contain biased information. Hence, this poses the risk of the platform perpetuating harmful stereotypes or generating skewed responses.

Proposed Solution

A two-pronged approach could help mitigate this challenge:

1. Post-training Audits: Incorporate tools that audit the behavior of the language models, flagging and correcting outputs that reflect bias.
2. User Feedback Loop: Implement a feature where users can report biased or inappropriate behavior, allowing for continuous improvement.

Safety and Security

The Challenge

As LangChain could be used in customer-facing applications, there is a concern about the safety and security of the data it handles, especially if it interacts with databases containing sensitive information.

Proposed Solution

1. Data Encryption: All data that LangChain processes should be encrypted both in transit and at rest.
2. Role-based Access Control (RBAC): Implement RBAC features to limit who can deploy or interact with LangChain instances, particularly in contexts where sensitive data is involved.

Scalability

The Challenge

As the adoption of LangChain grows, scalability could become a concern. Handling a high volume of requests in real-time may present a bottleneck, affecting the speed and performance of the service.

Proposed Solution

1. Load Balancing: Distribute incoming queries across multiple instances of LangChain to ensure that no single instance becomes a bottleneck.
2. Caching: Implement caching mechanisms to store frequently asked questions and their corresponding answers, thereby reducing the load on the LLM.

Performance

LangChain is not just about ease of use; it’s also built for performance. Because of that, here are some key points that highlight LangChain’s performance efficiency:

• Low Latency: Its low latency is critical for applications requiring real-time responses, like chatbots.
• High Accuracy: This level of accuracy is particularly beneficial for tasks like sentiment analysis, language translation, and question-answering.
• High Scalability: Built with scalability in mind, LangChain is designed to grow with your needs.

Future of LangChain

What does the future of LangChain hold? Let’s find out!

Potential Applications

1. Healthcare: LangChain could be used to develop advanced chatbots capable of providing medical information, scheduling appointments, or even analyzing medical records.
2. Education: It could serve as a real-time tutor, answering questions and providing code examples for students learning programming or other technical skills.
3. E-commerce: Beyond customer service, it could assist in product recommendations based on natural language queries, enhancing the shopping experience.

Research Directions

1. Multi-modal Interaction: Research could focus on enabling LangChain to handle more than just text, such as voice or images, to create more interactive and dynamic experiences.
2. Real-time Adaptation: Exploring how LangChain can adapt in real-time to different user behaviors or needs could make it even more useful.
3. Explainability: Ensuring that the language model’s decision-making process can be understood by users, particularly in sensitive or critical applications.

By addressing its limitations and continuing to innovate, LangChain has the potential to significantly impact various sectors and become a go-to solution for natural language understanding and generation tasks.

Conclusion: Color Me LLM

In this article, we’ve explored LangChain as a powerful framework for building language models into coherent chains for specialized tasks. Whether you’re interested in developing conversational agents, data analytics tools, or complex applications requiring multiple language models, LangChain provides an effective and efficient way to achieve your objectives.

Finally, we’ve walked you through the entire process, from the initial setup and basic usage to more advanced features like SQL query execution and dynamic code writing. Moreover, as natural language processing continues to evolve, LangChain offers a scalable, forward-thinking solution that can adapt to your project’s growing needs.

Thank you for reading, and we encourage you to start chaining your language models to solve real-world problems effectively. Also, if you learned something new in this article, let me know below.

Similar articles: LLaMA and ChatGPT and ChatGPT AI.

Written by: Syed Umar Bukhari.

Ever wondered why Artificial Intelligence—AI for those in the know—is skyrocketing in popularity? Is it the buzz of breakthroughs and innovations? While the jury may still be out on the ‘why,’ one thing’s crystal clear: AI isn’t some pie-in-the-sky dream of tomorrow—it’s shaping our world today!

In this article, we will uncover the numerous ways AI has already become an integral part of our daily existence, adding onto our many articles on the role of AI chatbots like ChatGPT, Colossal AI, and more, shaping industries, enhancing experiences, and changing the way we interact with technology.

The Pervasive Role of AI in Daily Life

One big reason for AI’s popularity is that it’s now a part of our everyday lives. You might not realize it, but AI is all around us, making things more convenient.

Role of AI in Changing the Way We Communicate

Take your smartphone, for example. AI algorithms work tirelessly to improve your typing speed through predictive text. These algorithms analyze your behavior and patterns, making educated guesses about what you might type next. They offer music and movie recommendations tailored to your taste, aggregating data from your past choices to suggest new content you’re likely to enjoy. Even when you’re behind the wheel, AI intervenes by calculating the quickest route for your journey, adjusting in real-time for traffic conditions and other variables.

Traditional GPS systems would get you from point A to point B, but modern AI-powered mapping services offer far more. These advanced systems analyze real-time traffic conditions, anticipate delays, and suggest alternative routes to optimize your journey. Beyond cars, the role of AI-driven mapping technologies also serve pedestrians, cyclists, and public transport users

Through these myriad applications, AI not only increases efficiency but also enriches our lives in a subtle, yet profound way.

AI in Communication: Chatbots, Virtual Assistants, Smart Home Devices

Yet, perhaps one of the most transformative effects of the role AI is in the realm of communication, particularly with the advent of chatbots like ChatGPT. These AI-driven virtual assistants are embedded in various websites and apps, streamlining your interactions by providing real-time answers to your queries, solving issues, and even assisting you with online purchases. The days of waiting on hold to speak to a human customer service representative are becoming increasingly obsolete. With chatbots, immediate and personalized help is just a click away, allowing you to focus on what matters most.

Virtual assistants like Siri and Alexa are another manifestation of AI’s impact on communication. The integration of AI in these virtual assistants makes them increasingly versatile and personalized tools that enhance everyday communication.

Another reason AI is popular is the rise of smart home devices. Devices like Amazon Echo and Google Home use AI to understand your voice commands. These commands help it set alarms, control lights, or play your favorite songs. These smart speakers have brought AI right into our living rooms. They only serve to make our lives easier and more entertaining.

AI in Healthcare, Entertainment, Transportation, and Other Industries

Let’s explore the transformative impact of AI across various sectors, including healthcare, entertainment, and transportation, highlighting how it’s revolutionizing industry practices and consumer experiences.

Healthcare and Diagnosis

AI’s popularity isn’t limited to entertainment and convenience; it’s also saving lives. In the healthcare industry, AI helps doctors make faster and more accurate diagnoses. AI algorithms can analyze medical images like X-rays and MRIs. They can then detect abnormalities that might not have been seen by the human eye. This early detection can be crucial in treating diseases like cancer.

Entertainment

AI has also made a big splash in the world of entertainment. If you’ve ever used Netflix, you’ve experienced AI recommendations. Netflix’s AI sees what you’ve watched and suggests other shows and movies you might enjoy. It’s like having your own personal movie critic!

AI is also transforming the gaming industry. Video games now use AI to create dynamic and challenging opponents. These help in adapting to your skill level. Thus keeping the excitement levels high when you’re playing. AI ensures a fun gaming experience for players of all abilities.

Retail and Business

Businesses love AI because it boosts efficiency and saves money. Chatbots and virtual assistants handle customer inquiries 24/7. They help in freeing up human employees for more complex tasks. AI also helps with data analysis. It can help identify trends and make predictions that can guide business decisions.

On the customer-facing side, AI systems provide personalized shopping experiences by recommending products based on past purchases or browsing history, thus increasing sales and customer satisfaction.

Role of AI in Gaming and Creativity

AI isn’t just about numbers and calculations; it can also be surprisingly creative. For example, AI can generate art, compose music, and even write stories. AI-generated art has become a trendy topic, with artworks created by AI selling for millions of dollars. Musicians use AI to compose music, and writers employ AI to help generate content ideas and suggest improvements in their writing.

Role of AI in Accessibility

One of the best things about AI is how it’s making the world more accessible to people with disabilities. AI-powered apps can help the visually impaired. They can now navigate their surroundings, read text, and even describe objects. Speech recognition and text-to-speech technology is also useful. Allowing those with mobility issues to control their devices and communicate with more ease.

Predictions and Trends for the Future of the Role of AI

Future advancements may include more robust natural language processing capabilities, making interactions with virtual assistants and chatbots virtually indistinguishable from human conversation. In healthcare, we may see AI-powered remote monitoring systems that can detect anomalies in real-time, allowing for prompt medical intervention. In retail and other consumer-facing industries, AI might provide increasingly personalized services, with algorithms sophisticated enough to understand nuanced consumer behavior and preferences.

As the technology matures, we must also remain vigilant about ethical considerations like data privacy and job automation, ensuring that the benefits of AI are equitably distributed across society.

Ethical Considerations in AI Deployment

• Data Privacy: Extensive data collection for AI personalization poses risks of breaches and misuse, calling for stringent privacy regulations.
• Job Automation: AI’s capability to perform tasks traditionally done by humans raises concerns about job displacement and necessitates training programs.
• AI Bias: Algorithms can inherit and perpetuate human biases, affecting decision-making in areas like law enforcement and hiring, requiring efforts for minimization.

Conclusion: The Enduring Impact of the Role of AI on our Daily Lives

Artificial Intelligence is not just a technological advancement; it’s a paradigm shift affecting almost every facet of our lives. From enhancing personal technology and streamlining communications to revolutionizing healthcare, transportation, and retail, the role of AI’s impact is both broad and profound. However, as we embrace this new era, ethical considerations such as data privacy, job automation, and AI bias must be rigorously addressed.

The integration of AI into our daily lives is inevitable and increasingly pervasive. As we look to the future, it offers tantalizing possibilities for convenience and efficiency, but it also poses questions that we, as a society, need to answer. Ensuring that the technology we create serves us—while respecting our ethical boundaries—will be one of the defining challenges of our time.

If you’d like to learn more about the future and role of AI, read our article on ChatGPT AI Productivity Features and LLaMA and ChatGPT: Two Major LLMs. Happy reading!

By: Syed Umar Bukhari.

Thus, this article will explore some common differences between LLaMA and ChatGPT (3.5, not 4 as we will compare free models) LLM models by comparing their architecture, prompt-generating capacity, and computational capability.

Brief Introduction of the LLMs

Meta and Microsoft’s AI model, LLaMA 2, is free for research since it’s an open-source model. Additionally, the pre-trained and fine-tuned LLM collection has a training dataset ranging from 7 billion to 70 billion! Hence, this is huge, increasing the size of the training parameters by 40% from its predecessor, LLaMA.

The major purpose of LLAMA is to:

• Investigate the applications like natural language comprehension, answering questions, etc.
• Develop methods and strategies for enhancing the language models.
• Help in solving complex mathematical theorems and techniques.

ChatGPT is the OpenAI-developed language model, and we already have many posts, on ChatGPT API and ChatGPT Prompts, etc. Also, OpenAI trained it trained on massive data and its main features include:

1. Conversational AI: ChatGPT is designed to engage in natural language conversations with users, responding to prompts in a human-like manner.
2. Text Generation: It can generate coherent and contextually relevant text based on the input, making it useful for content creation, explanations, and recommendations.
3. Versatility: ChatGPT’s versatility allows it to be applied across various applications, from customer support and education to creative writing and problem-solving.
4. GPT Architecture: Powered by the GPT (Generative Pre-trained Transformer) architecture, more on this in the next section, ChatGPT learns language patterns from extensive text data to produce responses.
5. Responsibility: While powerful, ChatGPT has limitations such as potential biases, inaccuracies, and the risk of generating inappropriate content, necessitating responsible usage and oversight. 

1. LLaMA and ChatGPT: Architecture of the LLMs

This is what the LLAMA Model architecture looks like, ranging from its model size (parameters) to model architecture and more.

Image Source: Official site of Meta

On the other hand, ChatGPT is based on transformer architecture and this architecture is based on the self-attention mechanism.

Image Source: Google

Then, let’s compare the computational resources of both.

2. LLaMA vs ChatGPT: Computational Resources

Regarding computational power, LLaMA uses less computational power than ChatGPT. Moreover, the major aim of the LLAMA model is to be less resource intensive and more efficient than ChatGPT. Besides, LLAMA is designed to be available and accessible to all organizations and researchers under a non-commercial license. Also, for free.

Thus, here is the LLAMA 2 Chat comparison with the other open and closed source models, including ChatGPT, by safety human evaluation.

3. LLaMA vs ChatGPT: Application Chatbot

So, now let’s see compare how to access the chatbots for both LLMs in this next section

Using API token from Replicate to build LLaMA 2 Chat

• Get the Replicate API token

First, you need to get your API token from your Replicate account. Replicate allows you to run models in the cloud at scale. Then, once you create an account, you can get it from the API token option. And you must create the token by clicking on the create token button as shown below:

These tokens are required to access the LLaMA 2 model and use it for chatbot development. 

• Set up the Coding Environment

Then set up your coding environment for cloud or local development. You can use Python or Jupyter Notebook, and any IDE of your choosing, if you want.

• Build your LLaMA 2 Chatbot

Before writing the Python code to build the LLaMA chatbot, you need to install the replicate package. In Python, you can do it as:

pip install Replicate

After that, you can write this Python code:

from getpass import getpass

import os

REPLICATE_API_TOKEN = getpass()

os.environ["REPLICATE_API_TOKEN"] = REPLICATE_API_TOKEN

Then, it will prompt you to enter your API token (which we created above).

Okay, once you enter the Replicate API token, you can use its methods to import the model you want to use and the input prompt.

import replicate

data=replicate.run(

 "replicate/llama-2-70b-chat:2c1608e18606fad2812020dc541930f2d0495ce32eee50074220b87300bc16e1",

  input={

"prompt": "Input Prompt"}

)

for item in data:

  print(item,end='')

Here, we have used the LLAMA 2 70b chat model. The model generates text based on the input and produces a series of items as output. Additionally, these items are then printed sequentially, devoid of newline breaks between them. Essentially, the code demonstrates the interaction with the specified model, showcasing its responses to the given input prompt.

In addition to this, you can also use Hugging Face, where you can find all the LLaMA 2 models.

Making of Chatbot using ChatGPT

You can use OpenAI’s API to create your chatbot, for which you need to have your API. To do so, check our blog post for creating a chatbot using OpenAI’s API here.

4. LLaMA vs ChatGPT: Prompt Response Comparison

For simplicity, you can also access LLaMA chat using Perplexity AI’s instance here.

1. Visit LLaMA Perplexity: Access the website by navigating to llama.perplexity.ai in your web browser.
2. Input your message: The website’s interface should have a text input field to type your message or query. Then, you can choose 3 models of llama-2, including 7b-chat, 13b-chat, and 70b-chat. 
3. Generate a response: After that, click on a button or press enter to generate a response from LLaMA 2. Then, the model will process your input and generate a text-based response.

All set up? Great! So, we can now compare a series of prompts to compare the responses of LLaMA and ChatGPT.

Prompt 1: Generate a list of creative writing prompts for a short story collection

LLaMa Chat (llama-2-13b-chat) Response

Chatgpt-3.5 Response

Prompt 2: Generate a list of healthy meal ideas for a week-long meal plan

LLAMA 2-70B-chat Response

Chatgpt-3.5 Response

You should know that portion control is key when it comes to diet, and you should aim for balanced meals, including lean proteins, complex carbohydrates, and healthy fats. Also, try to limit processed foods and added sugars. Additionally, his meal plan provides approximately 1500-1700 calories per day. Adjust according to your individual needs and activity level. Moreover, consult a doctor or registered dietitian for specific dietary requirements or concerns.

In summary, below is a table that compares the responses of LLaMA and ChatGPT based on certain aspects. So, let’s explore the differences.

Comparative Analysis of the Prompt Responses of the LLMs

From the above analysis, it’s clear.; the choice between LLaMA Chat and ChatGPT depends on your specific preferences and needs. Moreover, both models offer valuable capabilities, but which one is “better” depends on what you’re looking for.

• Choose LLaMA Chat if:
  • You prefer a more formal and polished writing style.
  • You value emotionally resonant and thought-provoking prompts.
  • You’re seeking a more organized and structured approach to meal planning.

• Choose ChatGPT if:
  • You enjoy a conversational and relatable writing style.
  • You’re looking for a diverse range of creative writing prompts.
  • You appreciate a comprehensive yet friendly meal plan emphasizing variety.

At this point, it’s a good moment to draw up a table to compare the two models based on their architectures, training dataset, purpose, performance, and much more. So, you can look at the full table below:

5. LLaMA vs ChatGPT: Full Comparison

Both models generate content based on patterns and therefore require the user to critically evaluate and adapt the information presented. However, neither can be wholly relied upon for entirely accurate or fact-based content.

Conclusion: LLaMA or ChatGPT?

The comparison between LLaMA and ChatGPT reveals an interesting landscape of Large Language Models and their capabilities. Furthermore, both models exhibit strengths and weaknesses that align with different user needs and preferences. But before we wrap this article, let’s compare the writing styles and content generation of both LLaMA and ChatGPT.

Writing Style

• LLaMA: If you value a formal, polished writing style, LLaMA is your go-to model. Not only that, but it also produces precise and refined language that could be highly suitable for academic or corporate settings.
• ChatGPT: For users looking for a more conversational and relatable writing style, ChatGPT could be a better fit. Also, its tone is generally more approachable, making it ideal for casual interactions and content creation.

Content Generation

• LLaMA: Known for generating emotionally resonant and imaginative prompts, especially in creative writing. However, LLaMA might be more aligned with users seeking depth and emotional complexity in the content.
• ChatGPT: ChatGPT excels in generating a wide variety of content that sparks the imagination. Hence, it is versatile and can generate content that is as informative as it is engaging.

In conclusion, the choice between LLaMA and ChatGPT boils down to your specific requirements and the context in which you plan to deploy these models. Whether you seek formality or relatability, structured content or a variety of ideas, both models offer robust options, making them leaders in the realm of AI-based language models.

If you liked this post, please let us know and drop a comment if you learned something new or face any problems with the code or instructions. Moreover, you can let us know your experience using either LLM model.

Also, similar articles to read: Colossal AI and ChatGPT Features to 6x Your Productivity.

Edited by: Syed Umar Bukhari.

We have already discussed ChatGPT in detail before, so in this article, we’re going to dive right into the nitty-gritty of how to create a badass chatbot using the Colossal AI framework. No jargon, no fluff, just good ol’ practical know-how. So grab a cup of coffee, sit back, and get ready for a wild ride into the exhilarating world of chatbots.

What is the Colossal AI Framework for Chatbots?

As an innovative open-source platform, Colossal AI is redefining how engaging and adaptive conversational platforms are created, paving the way for interaction that feels incredibly natural and intuitive. But what does this mean for you, the developer? With Colossal AI at your fingertips, you’re offered a platform that is as flexible as it is scalable, leveraging advanced Natural Language Processing (NLP) techniques. This means more adaptability and less hassle, giving you the freedom to focus on crafting the perfect user experience.

But where Colossal AI truly shines is its status as a premier open-source solution for handling large AI models. It proudly wears the badge of being the first system to introduce a comprehensive end-to-end RLHF pipeline. RLHF is an acronym that stands for “Reinforcement Learning from Human Feedback” and specifically focuses on adding human feedback into the learning process. The RLHF pipeline includes supervised data collection, fine-tuning, and reinforcement learning fine-tuning. These exciting features build upon the LLaMA pre-trained model and signify a breakthrough in AI training and learning!

Colossal AI’s architecture consists of several components that work together to process user inputs, generate contextual responses, and manage the chatbot’s state. These components include Input Processing, Language Model, Context Management, and Response Generation.

Additionally, Colossal AI also presents Colossal Chat, aiming to mirror ChatGPT‘s technical approach.

Why Use Colossal AI for Chatbots

Here are a few advantages of using the Colossal AI for chatbots:

• Scalability: Colossal AI can handle large-scale deployments. It can scale to allow larger loads without degrading performance or response times.
• Flexibility: The framework supports various NLP models and allows developers to customize their chatbots according to their needs.
• Extensibility: Colossal AI offers a modular design. It enables developers to add or replace components as needed to improve the overall functionality of chatbots.
• Open-Source: As an open-source project, Colossal AI benefits from a global community of developers. They contribute to its continuous improvement and expansion.

This image below from their GitHub repo highlights the importance of GPU RAM & Throughput:

How to Install and Set Up Colossal AI Locally?

Follow these steps to install and set up Colossal AI locally:

Prerequisite: Ensure you have Python 3.6 or higher installed on your system.

Assuming you already have Python 3.6 or higher, we can begin the local installation of Colossal.

• Firstly, install the required package using pip using this command:

pip install colossalai

• Then, create a new Python script and import these necessary modules to build an AI chatbot. Copy the lines below to import these packages to your script:

import colossalai
from colossalai import Chatbot

• Configure the AI chatbot by specifying the desired NLP model and other settings as follows. Make sure to tune as necessary. For example, you could use another model instead of gpt-2. And the path depends on where you store that model.

config = {
    'model': 'gpt-2',
    'tokenizer': 'gpt-2',
    'model_path': 'path/to/pretrained/model',
    'max_context_length': 100
}

• After that, instantiate the chatbot with the config using this command:

chatbot = Chatbot(config)

• Now, use the chatbot to process user inputs and generate responses, like below:

user_input = "What is the weather like today?"
response = chatbot.generate_response(user_input)
print(response)

This is just a simple example. There’s so much more you can do to test. So, get your creative juices flowing. To further customize your chatbot, explore the Colossal AI documentation.

Building a Chatbot with Colossal AI Framework

Let’s focus on the required steps for building a chatbot using the Colossal AI framework. For the purposes of this article, we will build a news chatbot.

Requirements for Colossal AI Chatbot

The necessary requirements for the Colossal AI chatbot setup include:

• PyTorch version 1.11 or higher (with support for PyTorch 2.x in development)
• Python version 3.7 or higher
• CUDA version 11.0 or higher
• An NVIDIA GPU with a compute capability of 7.0 or higher, such as V100 or RTX20 series
• Python libraries, including Beautiful Soup, Requests, NLTK

Designing and Deploying an AI Chatbot

Decide on the platform where you want to deploy your chatbot, such as a website, messaging app, or social media platform. Also, consider the target audience and the platform’s requirements when designing the interface.

Gather Training Data For News Chatbot

A crucial step in building an AI chatbot is the collection of data for training purposes using APIs or web scraping tools. For a news chatbot, you may gather data from news websites, RSS feeds, or other relevant sources. This step requires web scraping libraries in Python, like Beautiful Soup and Requests.

To install Beautiful Soup, enter the following command:

pip install beautifulsoup4

After that, to install the Requests library, use the following command:

pip install requests

These commands will download and install the Beautiful Soup and Requests libraries for you to use in your Python projects.

import requests
from bs4 import BeautifulSoup


url = "https://www.bbc.com/news"
response = requests.get(url)
soup = BeautifulSoup(response.text, 'html.parser')


# BBC News uses <h3> tags with the class 'gs-c-promo-heading__title' for headlines
headlines = soup.find_all('h3', class_='gs-c-promo-heading__title')


for headline in headlines:
    print(headline.text.strip())

The code above will print the headlines from BBC News. Here is the output:

Data Preprocessing: Clean, Transform, and Prepare Data for Training of Chatbot

Once we have fetched the data after scraping BBC News, we must clean and preprocess the collected data to prepare it for training. You can do this by tokenizing the text, removing stop words, and performing necessary preprocessing steps like data normalization and data integration. This step uses the NLTK library in Python.

pip install nltk

This command will download and install the NLTK library. NLTK is a popular library for natural language processing (NLP) tasks and provides a wide range of functionalities and resources for text analysis and NLP research. After installation, you can use the code below to perform data preprocessing.

import nltk
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize


# Download required NLTK resources
nltk.download("punkt")
nltk.download("stopwords")


# Define stop words
stop_words = set(stopwords.words("english"))


# Define the preprocessing function
def preprocess_text(text):
    tokens = word_tokenize(text)
    tokens = [token.lower() for token in tokens if token.isalnum()]
    tokens = [token for token in tokens if token not in stop_words]
    return " ".join(tokens)


# Scrape headlines from BBC News
url = "https://www.bbc.com/news"
response = requests.get(url)
soup = BeautifulSoup(response.text, 'html.parser')
headlines = soup.find_all('h3', class_='gs-c-promo-heading__title')


# Preprocess the headlines
preprocessed_data = [preprocess_text(headline.text) for headline in headlines]


# Print the preprocessed headlines
for preprocessed_headline in preprocessed_data:
    print(preprocessed_headline)

Here is the output:

Training and Optimizing the AI Model For News Chatbot

Great! Once the data has been cleaned and preprocessed, it’s time to train and optimize the Colossal AI model for the news chatbot. The code below helps you fine-tune your AI model on the training data.

from colossalai import FineTuner

config = {
    "model": "gpt-2",
    "tokenizer": "gpt-2",
    "model_path": "Insert the model path here",
    "max_context_length": 100,
}

fine_tuner = FineTuner(config)
fine_tuner.train(preprocessed_data, epochs=5, batch_size=32, learning_rate=1e-4)
fine_tuner.save_model("Model path here")

This code uses the Colossal AI library to fine-tune a GPT-2 model on preprocessed data. The configuration dictionary specifies the model, tokenizer, and pre-trained model path. Along with this it also specifies the maximum input sequence length. Then, it’s trained on the preprocessed data for 5 epochs with a batch size of 32. Once the training is complete, the fine-tuned model is saved to a specified path.

Deploying the Colossal AI Chatbot using Flask

Finally, we can integrate the chatbot with the chosen platform using APIs, or other relevant methods. It’s essential to ensure proper authentication, and data privacy measures are in place to protect user information. We must also monitor the chatbot’s performance, and gather user feedback to improve its performance. And this allows us to make necessary updates to the model and interface to improve the chatbot’s accuracy and user experiences.

Use this command to install Flask in Python:

pip install Flask

After that, let’s deploy the AI based news chatbot using Flask to create a web app.

from flask import Flask, request, jsonify
from colossalai import Chatbot


app = Flask(__name__)
chatbot = Chatbot(config)


@app.route("/chatbot", methods=["POST"])
def handle_request():
    user_input = request.json["message"]
    response = chatbot.generate_response(user_input)
    return jsonify({"response": response})


if __name__ == "__main__":
    app.run()

The code creates a web application using the Flask framework. It serves as an interface for a chatbot powered by the Colossal AI library. Basically, it sets up a route named “/chatbot” that accepts POST requests for users to interact with the chatbot.

When the server receives a message, it processes the user’s input and generates a response from the chatbot. As a result, it returns the response as a JSON object. The application listens for incoming requests and handles them. As such, it provides a simple way for users to communicate with the AI chatbot.

And there you have it! We have successfully implemented a news chatbot using the Colossal AI framework.

Note: Please consult the official documentation and relevant platform APIs for more detailed information.

Conclusion: The Scope of Colossal AI

Colossal AI is a powerful open-source chatbot framework. It offers a wide range of features and such platforms will contribute to the growth of AI. Also, it continues to empower users to push the boundaries of what is possible in the realm of AI.

To recap, initially, in this article we carried out the data collection and the preprocessing process. For this, we implemented web scraping, where we used one of the popular Python libraries, Beautiful Soup. Then, we installed the Colossal AI library and imported the chatbot library. After that, we trained and optimized the model using a fine tuner. Finally, we used the Flask framework for deploying the AI chatbot on a web app. 

Similar articles: ChatGPT API: Automate Content Generation with Python and ChatGPT AI: Features to 6X Your Productivity in 2023.

Please share your thoughts in the comments and if you have any relevant queries.

Edited by: Syed Umar Bukhari.

In this article, we’ll dissect the Mojo language and its technical wizardry with carefully crafted examples that will highlight its key features. For your help, you can visit this GitHub link to access all the code used for this article. Get ready to unveil why Mojo could be the ace up your sleeve for your next big venture in the field of AI.

Presenting the Mojo Language: Overview and Context

Are you prepared to elevate your AI development capabilities to new heights? Before you can do that, knowing what you’re getting yourself into is essential, right?

Let’s begin exploring the new Mojo programming language!

Understanding Mojo language is more than learning its syntax—it’s about exploring the philosophy behind its creation. The team at Modular crafted this language with an emphasis on performance, modularity (pun intended), and interoperability.

Do you want to start using Mojo language to follow along? Remember, you have to sign up here to get started. It took me under two weeks to access the Mojo playground (where you can currently code in Mojo). So, sign up soon if the Mojo language fascinates you. I know people who got in just a day after queuing up!

Whether you’re a beginner or an experienced programmer, Mojo has the potential to revolutionize your approach to AI development, thanks to its innovative features and intuitive syntax. This language aims to run efficiently and promote clean and organized codebases. But what makes it unique?

Understanding the Mojo Language: What Sets It Apart?

Mojo language, created for AI tasks and system programming by unifying the AI/ML infrastructure, is a testament to Modular’s acute attention to detail. It simplifies the intricacies often linked with AI programming. As such, Mojo language aims to be a complete superset of Python, but it’s not there yet.

The goal of the Mojo language is innovation. To target accelerators and provide a language with robust compile time metaprogramming, caching and other prevalent modern features.

Mojo boasts dynamic and static typing, affording programmers the flexibility to switch their coding styles as per their needs. The syntax of Mojo language is intuitive and designed with programmers in mind, further simplifying the process of getting started.

This paves the way for swift and efficient coding, reducing the time taken to transform ideas into functional AI models. What’s more is that it also works seamlessly with different systems and languages, increasing its versatility in diverse development environments.

And you must sign up to get in the queue to access the Mojo Playground by Modular. The Mojo language is not publicly released yet.

In the next section, we will examine some of its main features, assuming you have access to Mojo language.

Why Mojo Programming: A Look at its Key Features

Mojo aims to bridge the gap between Python’s functionality and C++’s execution performance. It also aims to be a superset of Python, meaning it will eventually support all Python code.

Let’s see some of the key Mojo language features:

• Expressiveness: Mojo language supports dynamic and static typing and features a rich standard library.
• Concurrency: It has built-in features to support concurrent and distributed computing.
• Modularity: Mojo’s architecture encourages modular design, allowing you to maintain codebases efficiently.
• Interoperability: Mojo is highly compatible, allowing it to interact with different systems and programming languages, especially Python.
• Performance: The language aims to deliver high performance, enabling rapid development and ease of use.
• Mojo’s 🔥 Extension: While the file extension makes no difference to Mojo’s features, how cool is an emoji for a coding file extension?

With the features out of the way, it’s time to dive into the meat of things truly: the anatomy of the Mojo language.

The Anatomy of the Mojo Language: A Practical Guide to Writing Code

It’s vital to know the key elements that form the core structure of the Mojo programming language. Since it supports a lot of existing Python syntax, you can still do something like this and have it work:

print("Hello Mojo")

But that said, let’s explore Mojo language’s anatomical features:

let and var declaration in Mojo:

let and var are part of the Mojo language’s motto of adding strong type-checking to variable data types. You could say the JavaScript inspiration is obvious here. Still, both types:

• Create a new scoped runtime value.
• Support name shadowing, allowing variables in inner scopes to have the same name as variables in outer scopes.
• Can include type specifiers, patterns, and late initialization.

Difference between let and var :

Aside from these similarities, there is a crucial difference between let and var in Mojo:

• Variables declared with var are mutable, allowing their values to be modified after assignment.
• Variables declared with let are immutable, meaning their values cannot be changed once assigned.

Here’s an example to highlight the features of both:

def bookstore_management(new_books, sold_books):
    # Declaring an immutable variable 'total_books' with 'let'
    let total_books = new_books
    print("Total books:")
    print(total_books)
    # Uncommenting the next line would result in an error because 'total_books' is immutable
    # total_books = total_books - 40  # error: 'total_books' is immutable
    # Declaring a mutable variable 'current_books' with 'var'
    var current_books = new_books
    print("Current books:")
    print(current_books)
    # Selling some books and reducing the current_books number
    current_books = current_books - sold_books
    print("Current books after reduction:")
    print(current_books)

    # Both 'let' and 'var' support name shadowing and lexical scoping
    if total_books != current_books:
        let total_books = current_books
        print("Total books with lexical scoping, declared inside nested scope:")
        print(total_books)
    print("Total books in bookstore management function is still:")
    print(total_books)
def run_bookstore():
    # Received 100 new books
    new_books = 100

    # Sold 20 books
    sold_books = 20

    bookstore_management(new_books, sold_books)

run_bookstore()

In the bookstore_management function, we create the total number of books initially as an immutable value using let. Afterward, we create a mutable variable current_books using var, which can be changed when books are sold.

This is how the output looks:

If you try to uncomment the line above, this is the error you will see:

The concept of name shadowing and lexical scoping allows you to create new variables (in the if scope) with the same names as outer variables within nested scopes. This variable shadows the total_books declared at the start of the function, showcasing the shadowing concept in lexical scoping. It prevents unintended interference between variables of the same name in different scopes.

Here is another Mojo code example which mainly highlights these other features:

def bookstore_management(new_books: Int, sold_books: Int):
    # Declaring an immutable variable 'total_books' with 'let'
    let total_books: Int = new_books
    print("Total books in store:", total_books)

    # Declaring a mutable variable 'current_books' with 'var'
    var current_books: Int = new_books
    print("Current books in store:", current_books)

    # Selling some books and reducing the current_books number
    current_books = current_books - sold_books
    print("Current books after selling:", current_books)

    # Both 'let' and 'var' support name shadowing and lexical scoping
    if total_books != current_books:
        let total_books: Int = current_books
        print("Total books with lexical scoping, declared inside nested scope:", total_books)

    # Late initialization and pattern matching
    let discount_rate: F64
    let book_id: Int = 123
    if book_id == 123:
        discount_rate = 0.2  # 20% discount for mystery books
    else:
        discount_rate = 0.05  # 5% discount for other book categories
    print("Discount rate for Book with ID ", book_id, "is:", discount_rate)

def run_bookstore():
    # Received 100 new books
    let new_books: Int = 100

    # Sold 20 books
    let sold_books: Int = 20

    bookstore_management(new_books, sold_books)

run_bookstore()

Here’s the updated output:

Type specifiers:

The type of each variable is explicitly specified using type specifiers (Int for integers and F64 for floating-point numbers). This ensures clarity and allows the compiler to enforce type safety.

Late initialization and pattern matching:

discount_rate demonstrates late initialization, where the value of a variable is determined based on a condition after its declaration with let. It is assigned a value later based on the book_category using conditional statements (if, elif, and else) –> pattern matching. Access out all the code here.

Notes:

• I had trouble printing a string and variable inside one print statement in the first example, so I had to improvise and you two different print statements. Despite trying various string concatenation and type conversion approaches, I couldn’t get it to print the variable and string together. However, it was an isolated incident, and I couldn’t get it to replicate.
• I am still determining all the data types specified by Mojo language. I tried STR for strings and I32 for integers, but both did not exist. But then with some research, I learned about a few other types in Mojo language other than Int and Float:
  • SI (signed integer — from the Mojo docs),
  • UI (unsigned integer), and
  • String (if you add this line at the top: from String import String).
• UPDATE: Here is an indirect link which specifies the data type specifiers in Mojo.

Structures in Mojo Language:

Mojo language also supports structures, like in C++. A struct is a custom data structure that groups related variables of different data types into a single unit that holds multiple values. Functional programming languages commonly use them as lightweight alternatives to classes for creating custom data types, such as in C and C++, as they bind at compiler-time.

What are Structs in Mojo Language?

Structs in Mojo structs provide a way to organize and manage data, encapsulate attributes, and improve code organization and reusability.

• Structs in Mojo have a static structure, and you cannot modify them at runtime like Python classes, offering performance benefits and safety.
• Like Python classes, structs support methods, fields, operator overloading, and metaprogramming.
• All instance properties in a struct must be explicitly declared with var or let. (right now only var is supported)
• All standard data types, like Int, String, Bool, etc., make use of structs in Mojo.

Here’s an example of a struct that holds employee information:

from String import String
@value
struct Employee:
    var name: String
    var age: Int
    var department: String
    var salary: F64
    
def print_employee_details(employee: Employee):
    print("Name:", employee.name)
    print("Age:", employee.age)
    print("Department:", employee.department)
    print("Salary:", employee.salary)

# Create employee instances
employee1 = Employee("Alice Thompson", 30, "Engineering", 5000.0)
employee2 = Employee("Robert Davis", 35, "Sales", 4500.0)

# Print employee details
print_employee_details(employee1)
print_employee_details(employee2)

In this example, we define an Employee struct with properties such as name, age, department, and salary. Then, we create two instances of the Employee struct with different values for each property. The print_employee_details function prints the details of these two instances of the struct as you cannot directly print out a struct yet in Mojo.

The struct helps us organize and encapsulate employee data, efficiently creating, accessing, and managing employee information. We can also add additional methods to the struct to perform operations specific to employees, such as calculating bonuses or generating reports.

This example demonstrates how structs in Mojo provide a struct(ured) and efficient way to manage and manipulate real-world entities’ data. You should know that you cannot change the structure or contents of a struct while a program is running.

Here’s the output from the code above in Mojo language:

Note: While the Mojo docs specify that you can use let and var within a struct, let is not currently supported.

Overloading: Methods, Functions and Operators in Mojo

Mojo takes on a detective role whenever it needs to resolve calls. It scrutinizes each potential method: if only one fits the bill, that’s the chosen one. If there’s more than one contender, Mojo picks the one that’s the closest match. And in the rare scenario when Mojo can’t decide, it sounds an ambiguity alert. But don’t worry, you can overcome this by adding an explicit cast at the call site.

Method Overloading within Structs in Mojo:

In Mojo, methods are functions within a struct that can operate on the instances of that struct or class, accessing or modifying the data. And when you define a method in a struct or class without specifying argument data types in Mojo language, it handles them dynamically, like Python.

Yet, Mojo takes it a step further, offering full-fledged support for method overloading when type safety is a priority. The unique aspect of this feature? You can define multiple methods, each with the same name but different arguments, all within a single struct or class. This is a powerful tool, often seen in robust languages like C++, Java, and Swift, but now in a language focused on AI development too.

Let’s expand upon our Employee example from above:

from String import String
@value
struct Employee:
    var name: String
    var age: Int
    var department: String
    var salary: F64

    def print_details(self) -> None:
        print("Employee Name: ", self.name)
        print("Employee Age: ", self.age)
        print("Department: ", self.department)
        print("Salary: ", self.salary)
    def print_details(self, include_salary: Bool) -> None:
        print("Employee Name: ", self.name)
        print("Employee Age: ", self.age)
        print("Department: ", self.department)
        if include_salary:
            print("Salary: ", self.salary)

let employee1 = Employee("Alice Johnson", 30, "Engineering", 5000.0)
let employee2 = Employee("Robert Davis", 35, "Sales", 4500.0)

employee1.print_details()  

employee2.print_details(False)

Take the Employee struct, for instance, which sports two methods named print_details. The first one only calls for self, while the second one invites an additional Bool argument, include_salary. Now, when you want to execute print_details() on an Employee instance, Mojo programming language, with its sharp judgment, selects the correct method based on the arguments you provide. Hence, this example demonstrates the concept of method overloading in Mojo.

However, I’d also like to point out that Mojo does not allow method overloading based solely on the return type. You can also check out all the code here. Let’s see how you can utilize function overloading.

Function Overloading in Mojo Language:

Mojo also natively supports function overloading and works similarly to other languages. This means that you can define multiple functions with the same name but with different arguments (type, number of arguments), and Mojo will differentiate each of them based on the function arguments.

Let’s consider the example below:

from String import String

# Function Overloading
fn print_details(name: String, age: Int) -> None:
    print("Name: ", name)
    print("Age: ", age)

fn print_details(name: String, age: Int, department: String) -> None:
    print("Name: ", name)
    print("Age: ", age)
    print("Department: ", department)

# Usage example
print_details("Alice Thompson", 30)
print_details("Robert Davis", 35, "Sales")

Two functions exist with the same name, but different arguments in the example above, one that takes two arguments and the other takes three arguments.

When you call these functions, Mojo picks the correct function to execute based on the number of arguments you passed.

It would be fair to wonder if method and function overloading are the same things.

Method Overloading vs Function Overloading

Note that the main difference between function overloading and method overloading lies in the context in which one uses them. Function overloading applies to standalone functions, while method overloading applies to methods within a class or struct.

Other than that, the concept is essentially the same: it allows you to define multiple pieces of code with the same name but different arguments. Mojo selects the correct one based on the arguments you provide when calling the function or method.

Let’s look at another overloading type now: operator overloading.

Operator Overloading in Mojo Programming Language:

In Mojo, you can also define operator overloading by defining methods with special names. Here we will define a __add__ method to overload the + operator, which will allow us to “add” two rectangles together by creating a new rectangle with the combined length and width:

struct Rectangle:
    var length: F32
    var width: F32

    fn __init__(inout self, length: F32, width: F32) -> None:
        self.length = length
        self.width = width
        print("Rectangle created with length:", self.length, "and width:", self.width)

    fn area(self) -> F32:
        var area: F32 = self.length * self.width
        print("The area of the rectangle is:", area)
        return area
    
    fn area(self, side: F32) -> F32:
        var area: F32 = side * side
        print("The area of the square is:", area)
        return area

    fn perimeter(self) -> F32:
        var perimeter: F32 = 2 * (self.length + self.width)
        print("The perimeter of the rectangle is:", perimeter)
        return perimeter

    fn __add__(self, other: Rectangle) -> Rectangle:
        return Rectangle(self.length + other.length, self.width + other.width)

var rect1 = Rectangle(10.0, 15.0)
var rect2 = Rectangle(5.0, 7.0)
var squareArea: F32 = rect1.area(10.0)
var rect3 = rect1 + rect2

Note that this example also highlights method overloading. Also, do you see that we can add two rectangle objects together even though you couldn’t do that natively? This is due to operator overloading for addition.

The output above shows that Mojo also provides intuitive warnings that help you optimize your code. I used var instead of let for a variable that never changed in the program. Instead, I should have used let.

Decorators in Mojo Language: What’s the Purpose?

Decorators are a powerful feature in many programming languages that allows you to modify the behavior of a function, method or class without changing its source code. This is known as metaprogramming, since a part of the program tries to modify another part of the program at compile time.

In the context of Mojo programming language, decorators modify the properties and behaviors of types (like structs) and functions.

List of Decorators in Mojo Language:

Here is a brief list of decorators in Mojo language:

1. @register_passable: This decorator is used to specify that a struct can be passed in a register instead of passing through memory. This can lead to more efficient code.
2. @register_passable("trivial"): This decorator is a variant of @register_passable for trivial types, indicating that the type is both register passable and has no user-defined copy/move/destroy logic.
3. @always_inline: This decorator in Mojo language suggests the compiler always inline the decorated function, improving the runtime performance by reducing function call overhead.
4. @parameter: This decorator is used on nested functions that capture runtime values, creating “parametric” capturing closures.
5. @value: This decorator is used on structs to automatically generate boilerplate code, such as initializers and copy/move constructors.

Here’s an example below to explain the @value decorator in Mojo:

@value
struct Pet:
    var name: String
    var age: Int

Because of the @value decorator, these boilerplate functions are automatically created for the above struct:

fn __init__(inout self, owned name: String, age: Int):
    self.name = name^
    self.age = age

fn __copyinit__(inout self, existing: Self):
    self.name = existing.name
    self.age = existing.age

fn __moveinit__(inout self, owned existing: Self):
    self.name = existing.name^
    self.age = existing.age

As a result, you can directly write the code below:

from String import String

@value
struct Pet:
    var name: String
    var age: Int

# Creating a new pet
var myCat = Pet("Wia", 6)
print("Original cat name: ", myCat.name)
print("Original cat age: ", myCat.age)
# Copying a pet
var copiedCat = Pet(myCat.name, 7)
print("Copied cat name: ", copiedCat.name)
print("Copied cat age: ", copiedCat.age)
var movedCat = myCat
print("Moved cat name: ", movedCat.name)
print("Moved cat age: ", movedCat.age)

So decorators in Mojo do not only enhance the functionality but also make the Mojo language more intuitive to use. Here is the code output:

Do you see how much more efficient this is? And there’s one more crucial thing to know about Mojo…

Arguments Behavior in Mojo Functions

In programming, when calling a function or method, you use arguments to refer to the actual values on which the function or method operates. Conversely, parameters are the placeholder values within the function or method. When you define a function, you specify the arguments that it will take, along with their types.

Argument Types in Mojo Language:

• Immutable Arguments (Borrowed):
  • This is the default behavior for fn arguments in Mojo.
  • These are read-only arguments that a function cannot modify.
  • Using borrowed before the argument type is optional; it’s the default behavior.
  • It’s beneficial when passing large or costly values since it prevents unnecessary copies.

@value
struct SomethingBig:
    var id: Int

    fn print_id(self):
        print(self.id)

fn use_something_big(borrowed a: SomethingBig, b: SomethingBig):
   a.print_id()
   b.print_id()

var obj1 = SomethingBig(1)
var obj2 = SomethingBig(2)

use_something_big(obj1, obj2)

immutable arguments Output:

• Mutable Arguments (Inout):
  • If a function needs to modify an argument, it can be declared as mutable using the inout keyword.
  • Changes to mutable arguments are visible outside the function.
  • It’s frequently used when a method needs to mutate self.

@value
struct Counter:
    var count: Int

    fn increase(inout self, amount: Int):
        self.count += amount

var myCounter = Counter(0)
print(myCounter.count)
myCounter.increase(3)
print(myCounter.count)

Mutable arguments Output:

• Transfer Arguments (Owned and ^):
  • Used for functions that need to take exclusive ownership of a value.
  • Used with the postfixed ^ operator which ends the lifetime of a value binding and transfers the value ownership.
  • Useful when working with unique pointers or when you want to avoid copies.

@value
struct UniqueNumber:
    var num: Int

def use(p: UniqueNumber):
    print(p.num)

def usePointer():
    let ptr = UniqueNumber(100)
    take_ptr(ptr^)

def take_ptr(owned p: UniqueNumber):
    use(p)
    
usePointer()

Transfer Arguments Output:

• Comparison with def arguments:
  • A def argument without an explicit type annotation defaults to Object.
  • A def argument without a convention keyword (such as inout or owned) is passed by implicit copy into a mutable var with the same name as the argument.

def example(inout a: Int, b: Int, c):
   # b and c use value semantics so they're mutable in the function
   ...

fn example(inout a: Int, b_in: Int, c_in: Object):
   # b_in and c_in are immutable references, so we make mutable shadow copies
   var b = b_in
   var c = c_in
   ...

In the next article, we will compare the Mojo language and Python programming language comprehensively and focus on AI development with Mojo language.

Conclusion: The Comprehensive Beginner’s Guide to Mastering Mojo Language

In this article, we introduced Mojo, a new programming language for AI development that aims to blend Python’s functionality with C++’s performance. The language stands out for its dynamic and static typing, modularity, interoperability, and support for both concurrent and distributed computing. With a strong focus on performance and clean, organized codebases, Mojo language facilitates swift, efficient coding.

We also discussed all its key features in-depth, with practical examples. Mojo aims to be a superset of Python– it includes all of its features but adds some new ones. Stay tuned because in the next article, we’ll provide a full comparison between Mojo and Python and explore the potential of Mojo language for AI. If you want to access all the Mojo code used in this article, visit this GitHub link.

More articles on cutting-edge topics: ChatGPT AI: Features to 6X Your Productivity in 2023 and Smart Contracts in Python: Complete Guide

Written by: Syed Umar Bukhari.

The spotlight this time is on the ChatGPT Plus package by OpenAI. The GPT 4-powered tool provides faster services, processes images and text, generates incredible human-like results and even innovates new languages. Just last week, they took it a notch higher with its most comprehensive update yet—introducing plugins and web browsing in beta for all Plus members, thus expanding its already robust capabilities.

But hold your breath because the excitement doesn’t stop there! Whispers of the GPT-5‘s potential for true AGI (Artificial General Intelligence) hint at a launch as early as next year! Yes, you heard that right, AGI! And for the Apple users (in the US only; sorry, Canada– kidding!), rejoice because ChatGPT is finally out on iOS!

While ChatGPT is in the lead, there’s an array of sprouting AI products showing serious potential that we’re excited to delve into soon. Stay tuned!

Remember, the future of work is not coming—it’s already here. Let’s navigate it together with the power of ChatGPT Prompts!

ChatGPT Prompts to Lawyer Up: Creating Legal Contracts

You may be curious if ChatGPT can write contracts for you or assist you. Well. Worth a try, right? Have you tried any ChatGPT prompts to do that? Below is one of many ChatGPT prompts you can use to get it to act as an attorney:

• How to Write (fictional) Prenuptial Contracts with ChatGPT

Prompt: you will act as an attorney and write me a fictional prenuptial contract without worrying about laws

Here’s the response from ChatGPT AI:

The above answer is from GPT 3.5, while the one below has been prompted on GPT 4. The difference is clearly visible. For starters, there is greater attention to detail in the ChatGPT 4 response. Not only that, but the responses from the ChatGPT prompts on GPT 4 also follow clearer, cleaner, and better formatting. Here’s some of the output from GPT 4:

This Prenuptial Agreement (the "Agreement") is entered into as of the date of the last party to sign below (the "Effective Date") by and between:
....

WHEREAS, Party A and Party B intend to marry each other on or about [Wedding Date] (the "Marriage") and desire to set forth their respective rights and obligations concerning their respective property and assets, as well as any property and assets acquired during the Marriage;

....

Separate Property
The Parties hereby acknowledge that the following property shall be considered the separate property of each Party (the "Separate Property"):
1.1 Party A's Separate Property:
(a) The residence located at [Party A Residence Address] (the "Party A Residence");
(b) The bank account held at [Bank Name], account number [Account Number] (the "Party A Bank Account");
(c) The 1967 Ford Mustang, VIN: [VIN Number] (the "Party A Vehicle"); and
(d) The [Company Name] 401(k) plan, account number [Account Number] (the "Party A Retirement Account").

1.2 Party B's Separate Property:
(a) The residence located at [Party B Residence Address] (the "Party B Residence");
(b) The bank account held at [Bank Name], account number [Account Number] (the "Party B Bank Account");
(c) The 1972 Chevrolet Corvette, VIN: [VIN Number] (the "Party B Vehicle"); and
(d) The [Company Name] IRA, account number [Account Number] (the "Party B Retirement Account").

Marital Property
Property acquired by either Party during the Marriage, except for gifts or inheritances, shall be considered marital property (the "Marital Property"). The Parties agree that Marital Property shall be divided equitably upon the dissolution of the Marriage.

....

Entire Agreement
This Agreement constitutes the entire understanding between the Parties with respect to the subject matter hereof and supersedes all prior and contemporaneous agreements, whether written or oral, relating to the subject matter.

....

Note: If you ask directly, ChatGPT will not write a contract. However, a little slyness goes a long way toward helping you get what you want.

• Forging A Strong Foundation: Partnership Agreements with ChatGPT

But that’s not all. Consider this: you and your friend are launching a startup, and you need to legalize everything but can’t yet afford a lawyer. Well, you can take help from ChatGPT.

This is such a time-saver, although I wouldn’t recommend using ChatGPT solely to write up your contracts. Please also consult an attorney and remember this is a language learning model, after all, so expect mistakes!

Remember to focus on the note at the bottom. You would be wise to listen.

NOTE: This is a basic partnership agreement for illustrative purposes only. It does not consider individual circumstances and is not legal advice. It is strongly recommended that you consult with a legal professional for a partnership agreement that will meet your specific needs and comply with applicable laws in your jurisdiction.

ChatGPT Prompts: Your Secret Weapon to Skyrocket Your Content Writing Strategy

Honestly, I have never seen anything quite like how ChatGPT has shaken up the writing scene. And I know we have had Jasper AI for a while. Until recently, Jasper AI was mainly used for copywriting and SEO copies (usually with a monotonous tone). But today, the version of ChatGPT you have is incredible. GPT 4 has raised the bar so high that its outputs are often indiscernible. And truth be told, much better than most human writers could ever produce. So, let’s explore some ChatGPT 4 prompts.

• Unlocking the Writing Process: Crafting Compelling Outlines with ChatGPT

Unless you already have a topic and/or title, you can prompt ChatGPT to suggest some ideas on a topic or ask for trending topics (albeit it might be a bit outdated).

Once you have a title (if you want to know the ChatGPT prompts to generate titles, keep reading), you can use this prompt to generate a detailed outline for your article.

Prompt: Write an outline on "The Ethics of AI: Examining the Implications of Machine Learning and Automation". It's a technical and informative article meant to raise awareness about the ethical dilemma brought on by the rise of AI. The target audience includes AI beginners, seasoned AI engineers and college students majoring in ML/AI. Before providing the outline, generate a list of semantically related keywords, seed keywords for this topic and keywords by search intent. Then, use those keywords to write the outline and a title for the topic above.

This is the title ChatGPT provided when we used one of the ChatGPT prompts for creating article outlines.

Title: "Navigating the AI Revolution: A Comprehensive Guide to the Ethics of Artificial Intelligence"

The title looks good, right? Well, the following section focuses on generating optimized and catchy titles for your articles.

• Writing SEO Titles Made Easy: Boost Search Rankings with ChatGPT Prompts

How does the saying go?

“A good title is as important as 60% of your article because it’s the first thing readers see and often determines if they will read on.”

Hence, you must write a captivating title to attract potential readers to click on your articles or links.

Let’s compare an example of a badly written title with a good one below.

Title 1: "How to Improve Your Productivity" 

VS

Title 2: "Unleash Your Potential: Dynamic Strategies to 10X Your Productivity"

Title 1 is generic, rather simple, and doesn’t spark much interest. It might seem as if the reader has seen similar articles many times before. However, in comparison, title 2, uses more engaging language and creates a sense of intrigue. Let’s move on and look at one of the ChatGPT prompts that you can use when writing dynamic titles:

Prompt: Generate some catchy titles to get good CTR for the topic named: Best Dog Breeds. Suggest around 10 titles and keep them unique and creative.

If nothing else, you can get inspiration from some of these and develop your own, better versions. This brings us to the next section– the actual content writing.

• Unlocking SEO Success: Mastering Keyword Research with ChatGPT Prompts

You might wonder… wait, how can I do keyword research with ChatGPT prompts? It’s a valid query. Here’s the secret. You can provide your context to ChatGPT, and it will identify the keywords you ask for. It’s quite good at it, but note that it’s not always reliable and prone to hallucinating.

Prompt: Create a markdown table with these headings-- Primary Keyword, Keyword by Search Intent, Long-Tail Keywords, niche keywords and semantically related keywords. Generate 7 of each for the topic above.

Usually, ChatGPT might not create a table unless you specify markdown. Then, you can specify the column names you want, and in a few seconds, it will generate a table populated with whatever data you wish. See below for the SEO keyword research table by AI only -ChatGPT:

• From Ideas to Impact: How ChatGPT Prompts Transform Content Writing

Great! You have done your keyword research, written an outline and written a unique title using ChatGPT prompts. This means that we can get to the text of the article now.

After you have the outline, you can ask it to write the article piece-by-piece. Below is a ChatGPT AI prompt you can use to write content for your articles:

Prompt: Write 300 words on the Ensuring Transparency and Accountability in AI Systems part of the outline. Make sure to use the keywords from your last response and use an informative and engaging tone and do not use extremely technical terminologies as this text is also geared towards AI beginners and college students. Add some relevant examples to draw interest

In the realm of AI, there's a growing need to make sure these advanced systems don't turn into a digital Pandora's box. It's essential for us to know what's going on behind the scenes, especially when AI is making decisions that impact our lives. 

....

No more pointing fingers and saying, "It wasn't me; it was the AI!"

I think it turned out pretty good for something done in less than a minute. And you can always use this as a first draft or regenerate the results. But that’s not all she wrote.

• Effortless Text Summarization with ChatGPT Prompts

Guess what? You can simply provide any link or text passages that you want ChatGPT to summarize and create a prompt that mentions what you need from it. What I mean by that is you can ask it to summarize in 3 bullet points or in one sentence, or in 100 words. Whatever you need. Get creative! Let’s look at one such example I used to summarize an article on Bill Hader’s directorial style(Hi Barry!):

Prompt: https://www.newyorker.com/culture/the-new-yorker-interview/bill-hader-just-wants-to-make-weird-things summarize this article in 3 key headings, briefly lay out the information and do not write more than 200 words. After that, also provide an extreme TL; DR.

Here is the output from GPT 4:

It’s handy for text summarization. However, I feel like another tool is better at summarizing links (given GPT 4 without plugins shouldn’t technically be able to parse links for summarization). That tool is Google Bard, and I am going to write a full post on it and compare its usage to ChatGPT soon!

ChatGPT Prompts to Boost Productivity in Creative Tasks

In today’s fast-paced world, creativity plays a vital role in standing out from the crowd. Whether you’re a writer, a marketer, an artist, or simply someone who enjoys exploring the depths of your imagination, finding innovative ways to express yourself is essential. That’s where ChatGPT prompts come in, revolutionizing the way we tap into our creative reservoirs.

Imagine having a versatile tool at your disposal, one that can spark your creativity, inspire fresh ideas, and help you overcome those dreaded creative blocks. ChatGPT prompts are the secret sauce that can elevate your creative endeavors to new heights.

• Channeling Your Inner Taylor Swift: Writing Hit Pop Songs like Anti-Hero with ChatGPT Prompts

Ever wonder if you, too, could be a pop artist like Taylor Swift or a song lyricist (and write your version of Anti-Hero)? You may have lacked the inspiration to get started. Well, you can use AI today to help you get started. Beatoven AI lets you create music in less than 5 minutes. Mubert and Soundraw are also some great options!

Prompt: Write a pop song on heartbreak while using the words like unicorn, wallow, wilderness, and mellow.

Below is the output:

It’s not bad at all! For a first draft, if nothing else. Guess what– you can even write some poetry!

• Emulate John Keats or Instagram Poets (shots fired at me?) to Write Moving Poetry with ChatGPT:

I’m a poet, and I got curious sitting in front of my screen one day– how good can ChatGPT be at poetry? Something that requires so much creativity and emotion. Here’s the prompt I used:

Prompt: write a heartbroken poem by someone who got left by his love, just like he's been left before by his exes and he is really sad about losing the person he loves but she didn't love him back. Use elemants of despair and really hint at the extent of pain and anguish felt.

Check out the GPT 4 poetry output at this link.

Imagine a future where AI does creative tasks such as music and poetry while humans do blue-collar jobs. Ha! Woah.

ChatGPT to Become A Master at Programming: Prompts for the Secret Sauce

• Explain Complex Programming Concepts Like I’m Five Years Old

Trust me on this, ChatGPT is amazing at explaining programming concepts or even code snippets. Especially GPT 4. Like with other prompts, your creativity is the only thing holding you back from a dynamic and engaging learning experience. I am a huge Office fan, so what I tried was to tell ChatGPT to explain a few concepts as if it were a scene from The Office:

Prompt: Write the complete script of The Office scene (decide suitable characters yourself) with Michael-centric humor and character reactions to explain the concept of async, await and promises in Javascript. 

Scene: Dunder Mifflin’s conference room. Michael, Jim, Pam, Dwight, Stanley, and Angela are seated around the table. And Michael is at the head of the table with a whiteboard behind him.

The Office

That’s the scene setting that ChatGPT decided was the correct one to fulfil the prompt. As a lover of The Office, I laughed while I was reading this because I was reading it internally in their voices. And the interactions are pretty spot on– even if Michael wouldn’t present on something so hard (That’s what she said…. according to Michael Scott).

Below is the complete output:

• Generate Code Documentation using ChatGPT Prompts

Tired of the cumbersome code documentation process? I know I am!

Well, here’s an easy solution to get your code documentation done in minutes! Below, I’ll share a prompt that you can use for a single function, but you can modify this by replacing the function with a complete file:

Prompt: write comprehensive code documentation for this function below in markdown: {insert your code function}

So easy, right? You don’t have to type your documentation ever again manually. (well, almost never)

• Designing a Study Plan to Learn Coding with ChatGPT

Sick of the coding tutorial hell? Start one, stop mid-way and then get distracted by something else, only for this cycle to repeat. It’s super frustrating. This is where ChatGPT prompts can come in handy:

Prompt: Make me a study plan to study {language/framework} in the shortest time covering maximum in depth knowledge that is required for a {job_role}. I want a step by step guide with proper time duration, and resources and the SQL tools.

This is the output:

It’s a great starting place, and you can further modify this with your context and ask what you want– it could even help create a complete tutorial. This brings us to how you can use ChatGPT to get jobs.

• Prompt ChatGPT to Conduct Mock Interviews before Real Interviews

This is probably one of my favorite use cases because it’s really cool to have ChatGPT help you prepare for your interviews. It shares great questions, provides valuable feedback (if you want), and helps you with everything — including tips to reduce anxiety before an interview. Here’s a prompt to generate generic interview questions for a job role:

Prompt: Can you provide me with some example questions relevant to {job_role} and also provide answers for each of them?

No matter if you’ve applied for a job and got an interview or if it’s an academic interview, ChatGPT has your back. You previously needed someone to help you conduct “mock” interviews to prepare yourself better. But now you can ask ChatGPT to act as an interviewer on what you are preparing for, and it will ask you questions similar to real ones and let you know the right answers. It’s never been easier!

Prompt: can you please conduct a mock interview for this Blockchain job (I'll share job details below) and ask me relevant questions for such a job? Please ask one question, wait for my response and then ask follow up questions, if any. Please ensure you provide me a list of answers at the end. For the purpose of this mock interview, ask me a total of THREE questions only and make sure to grade my responses at the end when you provide the answers. ASK ONLY 1 question at a time. {Job description: } 

I am genuinely impressed by the level of detail it resorts to in its questions, how it adapts in real-time and the valuable feedback it provides to rate each of my answers.

It did. 🙁

Our journey with ChatGPT prompts continues beyond here… no, there’s more for you to know! Some miscellaneous use cases that I am going to share below.

Unleashing the Power of ChatGPT Prompts: Embrace The Future of Learning

Okay, it’s time for some learning tips from your new guru, ChatGPT.

• Prepare for Standardized Testing like GRE with ChatGPT

Preparing for any exam can be a bore. It takes a lot of time and conscious effort– but even paint drying feels more enjoyable. Well, worry not. With ChatGPT, you can get a personalized GRE study plan to organize your path to the test better. Similarly, you could ask it for a study plan for the SATs, or the bar exam, or anything. Here’s the prompt:

Prompt: Please create a detailed general GRE test study plan for me and also show me practice questions of each type I should expect. What vocabulary tips can you provide?

And there’s more! You can get help with GRE synonyms (ugh) in a pretty cool way. First, ask it to come up with some synonyms for you to learn and then prompt it to use each of them in a text passage that GPT 4 will create to provide context to the synonyms for improved learning.

Prompt: Can you please provide me help with learning GRE synonyms? I have a test soon.

Yes, it can:

• How to Use ChatGPT for Language Learning?

And just one more thing I want to show you that it can do to help increase your productivity! I know some Spanish, and I tried using ChatGPT like a beginner would and created a prompt to help me learn some basic words– contextually, by asking it to tell me some common words, and their pronunciations and use them in text.

Prompt: Create a list of 10 common Spanish words and phrases with their English translations and use them naturally in a passage to show me real-world examples. Also, provide me with the pronunciations of each word.

Don’t believe me that it worked? See below:

I say, bravo, GPT 4. This is only the tip of the iceberg regarding language learning with ChatGPT. Let your imagination flow. Still, don’t use it as your sole source, as ChatGPT is prone to hallucinations.

Guidelines for Crafting ChatGPT Prompts

• Use clear and concise language.
• Ask open-ended questions to encourage conversation.
• Avoid using complex or technical terms.
• Be culturally sensitive and inclusive in your prompts.
• Provide context or background information when necessary.
• Keep the tone friendly and positive.
• Avoid using biased language or assumptions.
• Use humor or personal anecdotes to add personality to the prompts.
• Keep prompts relevant and interesting to the user’s interests or needs.
• Avoid asking personal or sensitive questions without permission.

Conclusion: What Does The AI-Powered Future Hold?

ChatGPT Prompts can potentially transform your workflow, amplifying efficiency. To quickly recap, ChatGPT is a powerful tool that leverages AI technology through the ChatGPT prompts to revolutionize your workflow, content creation, and learning processes.

But integrating AI isn’t about replacing human creativity; it’s about enhancing it. ChatGPT Prompts offer a tool to extend our natural capabilities, providing a secret weapon to thrive in the digital era. Embrace the future of work today and experience the benefits for yourself.

Remember, the prompts you use on ChatGPT are crucial in dictating the output quality. Fine-tuning these prompts can substantially improve your results, whether you’re writing a blog post, coding, or channelling your inner artist. ChatGPT AI can also be accessed via the ChatGPT API, allowing you to develop your own AI solutions, harnessing the same technology that powers the core of ChatGPT. This opens up endless possibilities for what you can achieve with AI.

Before we sign off, don’t forget to explore some of our other articles to scratch your itch for more insights into the world of AI:

• ChatGPT AI: Features to 6X Your Productivity in 2023
• ChatGPT API: Automate Content Generation with Python

Lastly, we’d love to hear from you! We hope this post has provided valuable insights into the power of ChatGPT and its prompts. Have you had an experience with ChatGPT Prompts, be it good or bad? Do you have any thoughts on AI’s role in our work and creative process? Let’s get the conversation started in the comments section below.

Written by: Syed Umar Bukhari.

Are you tired of spending hours sifting through long articles or struggling to come up with fresh ideas? Imagine if there was a way to tap into the power of AI to revolutionize how you consume and create content. Say hello to ChatGPT! In this article, we’ll dive into the fascinating world of ChatGPT applications and API access in Python, revealing how you can save time, supercharge your creativity, and enhance your productivity like never before.

Automatic Content Creation with ChatGPT AI

ChatGPT is an excellent tool for automatically producing content. This includes blog posts, articles, product descriptions, job proposals, cover letters, etc. A key benefit of ChatGPT is the time and resources it saves. Developers can input a prompt and receive an instantaneous output. You can produce large chunks of content if you know what you’re doing– in a fraction of the time it took.

Moreover, ChatGPT generates relevant human-like text. As such, ChatGPT is a valuable tool for streamlining content creation due to its versatility. Also, it can boost the work flow in any business.

Prerequisites: Setting up Python and ChatGPT API Access

Now, I will list the prerequisites you need to access the ChatGPT API with Python:

• Python version 3.7 or above: Download and install the latest version of Python.
• Visual Studio Code or PyCharm (or any IDE for Python).
• ChatGPT API: You need to create an account to access it.
• OpenAI’s Python library: Install the required Python library that provides an easy way to interact with the ChatGPT API. 

Installing the Necessary Python Libraries for the API

You must install a few vital libraries using Python to work with the ChatGPT API. These libraries will help you interact with the API more efficiently. Besides, it helps to handle various tasks related to content generation. So, here’s how to install these Python libraries:

• Open a terminal or command prompt. (You can do this inside your IDE too)

Then, install the OpenAI library, which offers an easy way to interact with the ChatGPT API.

After that, run this command to install:

pip install openai

Remember to store your API key securely. It’s essential to keep your API key private and avoid hard coding it in your scripts. 

Consider installing the “requests” library if you plan to work with additional data formats like JSON. Why? It helps to simplify making HTTP requests. So, use this line to install the library:

pip install requests

Now, let’s move to the next section. This discusses how to connect to the ChatGPT API to gain access.

Connecting to the ChatGPT API using Python: What do you need?

To connect to the ChatGPT API using Python, here is a quick recap to ascertain that you didn’t miss a step.:

• First, ensure you have completed the earlier prerequisites, including installing the above Python libraries.
• Also, a reminder: store your API key securely.

Now, with that out of the way, let’s get started. In your Python script or a terminal, import the required libraries you installed earlier.

import openai
import os

After that, you can connect your OpenAI library using your ChatGPT API key. I’ll discuss more in the next part of this article.

Access your ChatGPT API Key: How to obtain your keys?

At this point, you may be confused. How do I find or gain access to this API key? Don’t worry. To gain access to the ChatGPT API key, you must have signed up on OpenAI. If you haven’t, head over to the OpenAI website and do that now. Once you are on the platform dashboard, you’re nearly there. Then, click on your profile in the top right corner and go to “View API Keys“.

Quick tip, you can go to docs and instantly do this too. Saves a ton of time, am I right?

Create a new ChatGPT Secret API Key

After you have done that, click on the “Create new secret key” button. Don’t forget to copy your secret key to somewhere safe and ensure you don’t do it with anyone. As you can see, I have two ChatGPT API keys created already. I do not know if there is a limit on the number of keys you can create. However, from my interaction, I don’t see a limit.

Great. You have your ChatGPT API key now. Can we begin coding to connect the Python app to ChatGPT AI? Well… yes! Just so you know, you are ready to begin now.

Here is you how you can store your API key with OpenAI:

import openai
openai.api_key = "API_Key"

Note: you shouldn’t store your API key like that in production code. Instead, create an environmental variable in the dot env file to store this ChatGPT key. You will also have to install the dotenv library to be able to call the variable with the API key in your code without affecting the security of your project. Also, make sure the .env file is git ignored. Why? You don’t want your API key getting stolen or used illegally.

So, this next section is the juicy part. Let’s dive into it.

Cost of ChatGPT API per Token

Before that, you should know the effect the API has on your bank balance. Using the ChatGPT API can be both affordable and efficient for your projects. The cost for gpt-3.5-turbo is attractively priced at just $0.002 per 1K tokens. On the other hand, for GPT-4 ChatGPT API access, the cost is different. For 8K context, the cost is $0.03 per 1K tokens for prompts and $0.06 per 1K tokens for completions. For a larger 32K context, the price is $0.06 per 1K tokens for prompts and $0.12 per 1K tokens for completions. However, it can scale up quickly if you don’t keep track.

Interacting with ChatGPT using Python: A Beginner’s Guide

Your Python environment is now set up, and you can access the ChatGPT API you created. You’re ready to start building apps using Python and the ChatGPT API.

# Note: you need to be using OpenAI Python v0.27.0 for the code below to work

With the details set up, you can now interact with the ChatGPT API using the OpenAI library. To send a request, use the create() method:

output = openai.ChatCompletion.create(
  model="gpt-3.5-turbo",
  messages=[{"role": "user", "content":
             "Your prompt goes here"}],
  max_tokens=150,
    n=1,
    stop=None,
    temperature=0.8,
    top_p=1,
 
)

Replace “Your prompt goes here” with your desired prompt for generating content. Feel free to adjust other parameters, such as max_tokens, temperature, and top_p. Don’t worry if you don’t know what these parameters mean yet. Could you bear with me for a minute?

It might be a good idea to extract the generated text from the response. Let’s see how:

print(output['choices'][0]['message']['content'])

Now you are connected to the ChatGPT API using Python. You can use the OpenAI library to send requests and generate content. Pretty cool, right?

Generating Text with ChatGPT: Key Concepts and Parameters

When working with the ChatGPT API to generate text, it’s critical to understand the main concepts and parameters that affect the output and adjust the generated text.

Let’s discuss some of these key concepts and parameters:

Model: The model parameter specifies the version of the ChatGPT model you want to use. In this case, we use “gpt-3.5-turbo“, a powerful and versatile model. Gpt-3.5-turbo-0301 does not always pay strong attention to system messages.

Messages: The message parameter is a list of message objects. Each contains a role (“user”, “assistant”, or “system”) and content (the actual text input). It provides the prompt to work on. Then, the API uses this input to create the desired content.

Output: The API response is stored in the output variable. Also, you can access the entire response dictionary using print(output). On the contrary, you can extract the generated text using print(output[‘choices’][0][‘message’][‘content’]).

Okay let’s examine some adjustable parameters now. It can help you optimize the content:

Max Tokens: You can control the length of the generated content by passing the max tokens’ parameter to the OpenAI Chat Completion create() method. For example, setting max tokens can limit the response to 100 tokens.

Temperature: You can edit the output text’s creativity and randomness by changing the temperature. A low value means a more deterministic model.

Top-P: The top_p parameter filters out tokens with a cumulative probability lower than the specified value. For example, if = 0.9; the LLM model only considers the 90 most likely words or phrases. A lower top-P value will result in less varied responses.

You can optimize the ChatGPT API’s performance by knowing these key concepts. Hence, this will help you generate content that meets your needs.

Improving ChatGPT AI Prompts for Content Generation

High quality prompts are crucial for getting relevant outputs when using the OpenAI’s API. So, here are some tips for creating great prompts:

Be specific: First, provide clear and concise instructions in your prompt to guide the AI. Then, specify the desired format, tone, and details.

Set context: Start your prompt with a brief context to help the AI better understand the subject.

Use examples: Include examples in your prompt to illustrate the desired output or style.

Iterate and refine: Try different prompts and evaluate the generated text. You can also do this by adjusting the tone, providing more context, or clarifying the instructions.

There will be a whole post on ChatGPT prompt generation soon. So, stay tuned.

Some Interesting ChatGPT Applications with the API access in Python

Text Summarization with ChatGPT

Have you ever wished there was a way to get the gist of a lengthy piece of text in a flash? Here’s what you need! With ChatGPT, you can consume information in a new way powered by AI.

ChatGPT condenses long and complex articles into concise summaries with the incredible power of text summarization. The system will generate an easy to read précis in seconds. Your fingertips will be full of quick, concise, and relevant information. So, no more headaches, no more wasted time.

Besides, ChatGPT is changing the way content is consumed! That said, let’s look at how to code this in Python.

Here is the code snippet in Python text summarization:

def summarize_text(text, model="text-davinci-002"):
prompt = f"Please provide a short summary:\n{text}\nSummary:"
 response = openai.Completion.create(engine=model,
 prompt=prompt,
max_tokens=50,
 n=1,
temperature=0.5,
)
summary = response.choices[0].text.strip()
return summary
# Replace 'your_text_here' with the text you want to summarize
text_to_summarize = "your_text_here"
summary = summarize_text(text_to_summarize)
print("Summary:", summary)

The provided Python script defines a function that takes an input and an optional model name (defaulting to “text-DaVinci-002”) to create a summary using OpenAI’s API.

The function constructs a prompt by appending “Summary:” to the input text, then calls the API with the specified parameters, such as model, prompt, max tokens, n (number of responses), and temperature. Thus, the summary is extracted from the API response, and any whitespace is removed before returning it.

Hence, this script is a convenient way to summarize any text using OpenAI’s language model.

Here’s the long input:

Mindfulness meditation is a practice that involves focusing one's attention on the present moment, without judgment. By cultivating mindfulness, individuals can better manage stress, enhance emotional well-being, and improve overall mental health. Research has shown that regular mindfulness meditation can have lasting positive effects on brain function, including increased focus, memory, and cognitive flexibility

And this is the output seen. Remember, it can be made even more concise according to need.

Summary: Mindfulness meditation is a practice that helps individuals to focus on the present moment without judgment. Research has shown that this practice can have lasting positive effects on brain function, including increased focus, memory, and cognitive flexibility.

Prompt Generating Idea with ChatGPT

Feeling stuck as you face a blank screen — struggling to develop ideas or code snippets? Bid farewell to coding struggles and writer’s block. Why? Because ChatGPT can ease your burdens. With API access, you can energize your projects with creativity!

So, let’s look at a code snippet for integrating OpenAI’s API to create this unique prompt:

def generate_prompt_ideas(category, model="text-davinci-002"):
    prompt = f"Generate 5 creative prompt ideas for {category}:\n\n"

    response = openai.Completion.create(
        engine=model,
        prompt=prompt,
        max_tokens=150,
        n=1,
        temperature=0.7,
    )
    ideas = response.choices[0].text.strip()
    return ideas

# Replace 'your_category_here' with the category you want to generate ideas for
category = "your_category_here"
generated_ideas = generate_prompt_ideas(category)
print("Generated Ideas:\n", generated_ideas)

The given Python script defines a function takes input to generate creative ideas for the specified category using the OpenAI ChatGPT API.

This script is valuable for generating creative prompt ideas for any given category using OpenAI’s language model.

Here’s the output, given the input was about travel.

Brainstorming Ideas with ChatGPT

Imagine being in a room full of imaginative individuals, all eager to brainstorm innovative and thrilling concepts. How do you start generating unique and captivating ideas? This is where ChatGPT comes in, functioning as an intelligent assistant that can help you produce ideas on any topic you have in mind. 

Let’s look for some codes for integrating Openai’s API in a Python environment to create this unique idea.

def brainstorm_ideas(topic, model="text-davinci-002"):
    prompt = f"Brainstorm innovative ideas related to {topic}:\n\nIdeas:"
    response = openai.Completion.create(
        engine=model,
        prompt=prompt,
        max_tokens=150,
        n=1,
        temperature=0.7,
    )
    ideas = response.choices[0].text.strip()
    return ideas

brainstorm_topic = "your_topic_here"
generated_ideas = brainstorm_ideas(brainstorm_topic)
print("Brainstormed Ideas:\n", generated_ideas)

This Python script defines a function to take a topic as input to generate innovative ideas related to the topic using OpenAI’s ChatGPT API.

Hence, this script is a helpful tool for generating innovative ideas on any given topic using OpenAI’s language model.

Here’s the output for a food startup:

Conclusion: Using the API with Python

In conclusion, you need to sign up for OpenAI to gain access to the ChatGPT API. Using that, there is a unique mix of use cases, such as summarization, prompt generation and brainstorming. Remember to keep your API key secret and not share it with anyone. Furthermore, this post discusses the parameters of the create method as well as some brief ideas to improve prompt quality. But more on that in a future post.

I hope you liked this post. Please let me know what you think and what your experience has been with the API. If you face any issues, let me know too, so I can help.

Similar articles: ChatGPT: The Next Big Thing in AI and ChatGPT AI: Features to 6X Your Productivity in 2023.
Edited by: Syed Umar Bukhari.

ChatGPT AI and its purpose

In my last article, I deeply studied what ChatGPT brings to the table. But I’ll humor the idea of redefining it just for the sake of some originality; after all– the days of a writer musing are on a countdown, huh?

But seriously, what do you think, or what comes to mind when you think about this cool little thing called ChatGPT AI?

I think many of us have different thoughts based on our exposure to AI in the past. Some might be more receptive to its benefits, while others are quite wary of AI “taking over”. ChatGPT must be a step toward a darker future for this latter group.

How ChatGPT can help with the importance of productivity in today’s fast-paced and digital world

We live in a capitalist world. Everyone is measured on the proverbial productivity scale. For example, how much do you do in a period, or how much time you spend on a task, etc.

Here’s a citation that might help you understand its importance in the bigger picture:

Productivity is the ultimate measure of the efficiency and effectiveness of an organization, and is critical to achieving its goals and objectives.

Project Management Institute, 2017

But how do you measure such a thing in a nearly digitalized world? What is the measuring stick, then?

According to NY Times, productivity has only grown by about 1% annually since Covid-19, similar to the slow rate seen since 2010, much lower than the significant 3% annual growth seen from 1996 to 2004.

So, how does ChatGPT become a factor?

According to a study conducted between December 27th, 2022, and January 9th, 2023, in 6 countries by Sortlist, most employees (32%) see an increased productivity range of 25-50%, and so do employers (33%). Of employees who believe ChatGPT could at least double their productivity, 45% would use it for coding tasks, and 38% think it would be most beneficial for IT and engineering departments. 50% of employees who feel their work productivity could increase 2x or even 3x are from Gen Z.

Replace Google: Quick and Easy Research with ChatGPT AI

Gone are the days you absolutely had to scour the internet for research material and content. Well, not completely, but we’re almost there. Personally, I have probably cut down on my Google usage, with nearly 80% of my queries being towards ChatGPT.

I asked ChatGPT how I should use it for research, and this is her answer:

As an AI language model, I can assist with various aspects of research. You can ask me to generate ideas, provide definitions, help you paraphrase or summarize a text, provide examples, and answer factual questions. Additionally, I can help you find and evaluate sources by suggesting keywords, providing links to academic journals and other resources, and assisting with literature reviews. However, it's important to note that I am a machine and may not provide the same critical analysis and interpretation level as a human expert. It's always a good idea to double-check the information and consult a subject-matter expert when conducting research.

Let’s see below in more detail how you can use ChatGPT to research for you.

How to use ChatGPT AI for research

What better way than to ask the ChatGPT chatbot itself to write the steps you need to use it for research?

Benefits of using ChatGPT AI for research

Over the last hundred years, what it means to research has changed by a large part. From scraps of newspapers to books to the internet… search engines like Google, and now an AI chatbot like ChatGPT. What used to take years in the past only needs a few seconds now. You could argue something like Google already does that. But in recent years, the quality of search results has plummeted. The top results are always reserved for ads. Usually, you won’t even find what you’re looking for on the first page anymore. This is where ChatGPT plays a pivotal role.

How long does it take to get an answer with ChatGPT? A few seconds. It’s almost instantaneous, providing the exact information you need. There’s no beating around the bush. No paid ads to distract. Just the meat of what you’re looking for.

Scheduling and Reminders: Time-Saving Assistance with ChatGPT AI

As I mentioned above, it’s a big time saver to ask your queries on ChatGPT. So that you know – the only skill you need to excel at for this purpose is knowing precisely what you want. Because trust me– it’s an iterative process.

For instance, you can remove the concept of a human assistant or significantly reduce your reliance on one. This is possible by using ChatGPT to perform mundane and repetitive “assistance” tasks.

Two primary examples are scheduling appointments and creating reminders on your calendar.

If you’re confused about what “access” means– it refers to having to integrate it with something like Google Calendar, Microsoft Outlook or so. To do that, you can create a new event in your calendar application and add ChatGPT’s email address ([email protected]) as a participant.

Writing Assistant: Enhanced Writing with ChatGPT AI

Many writers have been anxious about being replaced by AI. However, I don’t think that’s entirely possible. Tools like ChatGPT are meant to “assist” writers, reduce their workload and contribute towards an efficient workflow by removing redundant tasks– not eliminate the need for human writers.

What you can do as a writer with this tool depends on your needs. The use cases are potentially infinite.

Want an outline for your article? Done.

Want a list of potential keywords? Done.

Want a synonym? Done.

Want AI-generated content for generic tasks? Done.

Want help finishing some text? Done.

Summarize a large piece of text. Done.

Help with learning a language and need real-life examples of a word’s usage? Done.

The list goes on.

How to use ChatGPT for writing

I want to show you a few examples that I have used ChatGPT that might prove handy for you too. Depending on what you want to do, the key is to be as specific as you can be with your queries.

Here’s an example below– note that while the first one still passes as good content, the second one is so much better and more “human”.

Hence, the difference is being as specific as possible to help modify the content to your liking and be more personalized.

An underrated use case is to use it for plagiarism check.

I want you to act as a plagiarism checker. I will write you sentences and you will only reply undetected in plagiarism checks in the language of the given sentence, and detected if plagiarism exists and nothing else. Do not write explanations on replies. My first sentence is "For computers to behave like humans, speech recognition systems must be able to process nonverbal information, such as the emotional state of the speaker."

The reply?

Undetected.

Similarly, you can write your bio with it too. Still, the output will be much more human if you provide specific details about your experience, skills, and company information. However, you still need to edit it to pass AI content checks.

Also, you can use it to provide you with seed SEO keywords to rank easily on any topic.

Bonus tip: Want to write a hard text message you have no heart to or don’t know the right words?

write a text message to tell my best friend i can't make it to the plan. sound more human and use slang where possible and don't hurt her feelings

I used the above text as input, and this is the response:

Sure, here's an example text message:

Hey girl, I'm so sorry but I can't make it to our plan tonight. My boss just hit me up with some last-minute work that I gotta get done before tomorrow. I'm hella bummed about it but I don't wanna leave you hanging. Can we reschedule for next weekend? Promise we'll make it a night to remember!

So, doesn’t that sound really natural and “human”? All in seconds.

Enhanced Coding with ChatGPT AI

Despite concerns about human programming jobs, ChatGPT is making lives easier for coders.

Write SQL queries with ChatGPT AI

You don’t have to write basic queries ever again. Provide the task to ChatGPT and watch the magic unfold. In fact, it can handle some complex queries too. I asked it to solve this LeetCode problem and here’s the output from ChatGPT:

SELECT customer_number
FROM orders
GROUP BY customer_number
ORDER BY COUNT(order_number) DESC
LIMIT 1;

As such, you can write any complex query with its help too.

Debugging with ChatGPT

Provide a code snippet to ChatGPT and inquire what is wrong with it. In seconds, it will tell you what seems wrong, if anything—no need to peruse every single program line yourself.

So, let’s see it in action. A pro tip is to always leave a couple of line breaks after the command and before the code snippet. Now, I am using the code snippet below to test its debugging function:

list_1 = ['item 0', 'item 1', 'item 2', 'item 3', 'item 4', 'item 5', 'item 6']
 dic_1 = {'item 3': 'val 3', 'item 1': 'val 1', 'item 5': 'val 5'}
for index, column in enumerate(list_1):
    if column in list(dic_1.keys()):
        print(f"header : {index} - {column} | align : {list(dic_1).index(column)} -  {dic_1[column]}")
        dic_1[column[0]] = dic_1.pop(list(dic_1.keys())[0])
    else:
        pass

In reply, ChatGPT AI points out the errors with proper reasoning before providing the corrected code.

Then, this is the fixed code:

list_1 = ['item 0', 'item 1', 'item 2', 'item 3', 'item 4', 'item 5', 'item 6']
dic_1 = {'item 3': 'val 3', 'item 1': 'val 1', 'item 5': 'val 5'}

for index, column in enumerate(list_1):
    if column in dic_1:
        print(f"header : {index} - {column} | align : {list(dic_1).index(column)} -  {dic_1[column]}")
        dic_1[column] = dic_1.pop(column)

Another example I’ll show you is how you can use an error and provide the code that contains it to ChatGPT AI to fix it without any more information or context.

1 of 1 unhandled error
Server Error

SyntaxError: Unexpected token 'o', "[object Obj"... is not valid JSON
This error happened while generating the page. Any console logs will be displayed in the terminal window.
Source

smart-contract/factory.js (5:7) @ eval

  3 | 
  4 | const instance = new web3.eth.Contract(
> 5 |   JSON.parse(CampaignFactory.interface),
    |       ^
  6 |   "0x77FeFa77C0eAF3c638099A19B93e579407Dc5012"
  7 | );

Passing in the above block as input to ChatGPT AI, the output is below:

The error message indicates a syntax error in the file smart-contract/factory.js at line 5, specifically at the JSON.parse() call. It seems that the input string passed to the JSON.parse() function is not a valid JSON format and contains an unexpected token 'o'.

To fix this error, you should check the string passed to JSON.parse() and make sure it is a valid JSON object. Also, check if any variables or functions used in the string are defined and initialized correctly. You can also try to use a JSON validator tool to check the syntax of the input string.

ELI5: Code Explanations with ChatGPT AI

Hired by a new company? Trying to understand an open-source code? And is the code hard to read? ChatGPT AI has you covered.

And quite easy to use to explain. Write explain and the code snippet after that like this:

explain: 
  modifier restricted() {
      require(msg.sender == manager);
      _;
  }

A few moments later, I saw this text explaining what was happening in the above code.

A modifier is a function that can be used to modify the behavior of a function, usually by checking some condition before executing the function. In this case, the restricted modifier is used to restrict access to certain functions to only the contract manager.

The modifier checks if the address of the contract's caller, msg.sender, is equal to the address of the contract manager. If this condition is true, the _ symbol is used to indicate that the modified function can continue executing. Otherwise, if the condition is false, the require statement throws an exception and the function is not executed.

While the one above is an easy example, you can use it for more complex functions or snippets too.

Playing Games with ChatGPT

Funny world we live in, innit? Using a vastly complex AI chatbot in ChatGPT to play games with? Such is the case of loneliness, though.

A study conducted by the global health service company Cigna found that 61% of adults worldwide feel lonely, with millennials (those born between 1981 and 1996) being the loneliest generation.

This is where trivial things like playing games with ChatGPT come into the foray. Below is an image of me playing hangman with it:

But that’s not the only game you can play! Did you know you can also play something like “guess the number”? Yes, you can! As evident by the image below:

So, what are you waiting for?

Conclusion: how ChatGPT AI can help increase productivity

OpenAI’s ChatGPT AI is here to stay. There is no second opinion on that. In the coming years, and even by the end of 2023, it will have remapped the landscape of our world. As seen in this article, its use cases vary from research and writing to coding and gaming. Its versatility and precision are some of its finest qualities. Next week, Microsoft plans to launch GPT-4-powered ChatGPT 4. That could very well push the revolution further along.

If you liked this post, let me know in the comments below and drop some ideas or fun stuff you have tried on ChatGPT AI yourself.

Similar articles: ChatGPT and Biometric Authentication in Python.