Introduction to Prompt Engineering for ChatGPT

Self-learning is a diverse and personal journey, with individuals finding their own preferred methods. For avid readers, delving into articles and blog posts is an enriching experience, as it allows them to absorb knowledge at their own pace. On the other hand, visual learners thrive by watching YouTube channels, where dynamic explanations and demonstrations captivate their attention. Meanwhile, those who learn best through examples appreciate the power of seeing concepts in action. To cater to these varied preferences, we have created a comprehensive blog that guides you step by step. Each blog post includes a link to a complete Jupyter notebook for hands-on practice, ensuring a deeper understanding of the topic. Additionally, we provide a YouTube link to supplement the written content with engaging visuals. No matter your learning style, we've got you covered!

In our comprehensive programming series, we are thrilled to introduce you to the world of programming using ChatGPT and PythonAPI. Over the course of ten informative blogs, each accompanied by a dedicated YouTube video and Jupyter notebook, we aim to equip you with the skills and knowledge to become a proficient programmer. While each blog can be read independently, we highly recommend following the intended order for a seamless learning experience. Starting with the basics, we gradually build upon concepts, allowing you to grasp programming fundamentals before diving into more advanced topics. Don't forget to begin with the first blog, as it guides you through setting up your environment and getting started on your programming journey.

  • Intro to ChatGPT
  • Intro to Prompt Engineering
  • Advanced Prompt EngineeringT
  • Sentinement Analysis using ChatGPT

In this tutorial, we will walk you through how to use the ChatGPT Python API to develop programs. ChatGPT is a powerful language model developed by OpenAI, based on the GPT architecture. It is designed for tasks such as answering questions, generating text, and much more.


Before we begin, you should have some basic knowledge of Python and RESTful APIs. Familiarity with OpenAI’s GPT models is helpful but not required.

Setting up

To get started, you will need an API key from OpenAI. You can get one by signing up on the OpenAI website.

Next, install the openai library using pip:

pip install openai

Using the ChatGPT API

Once you have installed the library and obtained your API key, you can start using the ChatGPT API.

First, set up your API key and import the required library:

import openai

# Replace 'your_api_key_here' with your actual API key
openai.api_key = "your_api_key_here"

Sending a request to ChatGPT

Now, let’s create a function that sends a prompt to ChatGPT and returns the generated text:

def generate_text(prompt, model='text-davinci-002', max_tokens=150):
    response = openai.Completion.create(

    return response.choices[0].text.strip()

You can customize the model, max_tokens, and other parameters based on your requirements.

Example usage

Here’s an example of how to use the generate_text function:

prompt = "What is the capital city of France?"
response = generate_text(prompt)

This should return:

The capital city of France is Paris.

Handling a conversation

To simulate a conversation with ChatGPT, you can use a list of messages as input and append user messages and ChatGPT responses:

conversation_history = []

def ask_chatgpt(question):
    prompt = f"{question}\n{''.join(conversation_history)}"
    response = generate_text(prompt)
    conversation_history.extend([f"User: {question}\n", f"ChatGPT: {response}\n"])
    return response

# Example conversation
question1 = "What is the capital city of France?"
response1 = ask_chatgpt(question1)

question2 = "What is the population of Paris?"
response2 = ask_chatgpt(question2)

This example simulates a simple conversation with ChatGPT, asking two questions and receiving responses.


In this tutorial, we’ve introduced you to using the ChatGPT Python API for developing programs. You’ve learned how to send requests to ChatGPT and receive generated text, as well as how to simulate a conversation. You can expand on these examples to create more advanced applications that utilize the power of ChatGPT.

Remember to replace the API key placeholder with your actual API key before running the code. Happy coding!