How to Build Your Own Chatbot with GPT-3

Are you ready to take your chatbot game to the next level? Look no further than GPT-3, the powerful language model from OpenAI. With GPT-3, you can create chatbots that are able to understand and respond to natural language like never before.

In this article, we'll walk you through the process of building your own chatbot with GPT-3. From setting up your development environment to training your model, we've got everything you need to know to create a chatbot that will amaze your users.

What is GPT-3?

Before we dive into the nitty-gritty of creating a chatbot with GPT-3, let's take a moment to talk about what GPT-3 actually is.

GPT-3 (short for "Generative Pre-trained Transformer 3") is a language model developed by OpenAI. It's designed to be able to generate human-like responses to text prompts. This means that you can give GPT-3 a piece of text, and it will generate a response that is contextually relevant and grammatically correct.

One of the most compelling things about GPT-3 is its ability to understand and respond to natural language. This means that you don't need to spend hours crafting specific responses for your chatbot - instead, you can just give it a general prompt and let GPT-3 do the heavy lifting.

Getting started with GPT-3

Before you can start building your chatbot with GPT-3, you'll need to set up your development environment. Here's what you'll need:

To get started, you'll need to sign up for an OpenAI API key. You can do this by visiting the OpenAI website and following the instructions. Once you've got your API key, you can start setting up your development environment.

First, create a new Python virtual environment. This is optional, but we recommend it to ensure that you don't accidentally mess with the dependencies of other Python projects you may be working on. You can create a new virtual environment by running:

python -m venv myenv

This command will create a new virtual environment in a directory called myenv. You can activate the environment by running:

source myenv/bin/activate

Once you've activated your virtual environment, you'll need to install the openai package using pip. You can do this by running:

pip install openai

Now that you've got everything you need set up, let's move on to creating your chatbot.

Training your chatbot with GPT-3

The first step in creating your chatbot with GPT-3 is to train your model. To do this, you'll need to provide GPT-3 with a dataset of text prompts and responses.

One of the easiest ways to do this is to use OpenAI's "Playground" tool. The Playground allows you to interact with GPT-3 directly, giving you the ability to provide text prompts and see how GPT-3 responds. By using the Playground, you can quickly and easily generate a dataset of text prompts and responses that you can use to train your chatbot.

To get started with the Playground, head over to the OpenAI website and log in to your account. Once you're logged in, navigate to the "Playground" tab.

In the Playground, you'll see a text box where you can enter prompts for GPT-3. Try entering a few different prompts and see how GPT-3 responds. For example, you might try:

As you enter prompts, you'll see that GPT-3 generates responses in the text box below. You can copy these responses and use them as training data for your chatbot.

As you generate more and more training data, you can start organizing it into a format that can be fed into GPT-3 using the API. One common format for chatbot training data is the "conversation" format, which looks something like this:

    "prompt": "What's your name?",
    "completion": "My name is GPT-3."
    "prompt": "Tell me a joke.",
    "completion": "Why don't scientists trust atoms? Because they make up everything!"
    "prompt": "What's the weather like today?",
    "completion": "I'm sorry, I don't have that information."

Once you've got your training data organized in this format, you can start training your chatbot using the OpenAI API.

To do this, you'll need to set up your OpenAI API credentials as environment variables. You can do this by running:

export OPENAI_API_KEY=your-api-key

Replace your-api-key with your actual API key.

Now, you can create a new Python file and start training your chatbot. Here's an example:

import openai
import os

openai.api_key = os.getenv("OPENAI_API_KEY")

def train_chatbot(data):
    prompt_list = []
    completion_list = []

    for d in data:

    prompt = "\n".join(prompt_list)
    completion = "\n".join(completion_list)

    response = openai.Completion.create(

    for choice in response.choices:
        result = choice.text

    return result

This code defines a train_chatbot function that takes in a list of data in the conversation format and returns a response from GPT-3. To use this function, you can simply call it with your training data as an argument:

data = [
        "prompt": "What's your name?",
        "completion": "My name is GPT-3."
        "prompt": "Tell me a joke.",
        "completion": "Why don't scientists trust atoms? Because they make up everything!"
        "prompt": "What's the weather like today?",
        "completion": "I'm sorry, I don't have that information."

response = train_chatbot(data)

Congratulations - you've just trained your own chatbot with GPT-3!

Putting it all together

Now that you've trained your chatbot, it's time to put everything together and create a real chatbot that can interact with users.

To do this, you'll need to create a web service that can receive text prompts from users and respond with text generated by GPT-3. There are many different ways to do this, but one popular approach is to use a chatbot platform like Facebook Messenger or Telegram.

Here's an example of how you might set up a chatbot using the Telegram API:

  1. Create a new Telegram bot by following the instructions on the Telegram website
  2. Use the Python python-telegram-bot library to set up a webhook that listens for messages sent to your bot
  3. When a message is received, pass it to your train_chatbot function to generate a response
  4. Send the response back to the user using the Telegram API

Here's some example code to give you an idea of how this might work:

import telegram
from telegram.ext import Updater, CommandHandler, MessageHandler, Filters
import os
import openai

openai.api_key = os.getenv("OPENAI_API_KEY")
bot = telegram.Bot(token=os.getenv("TELEGRAM_BOT_TOKEN"))

def train_chatbot(data):
    # ... see previous example code ...

def start(update, context):, text="Hi there! I'm a chatbot powered by GPT-3. What can I help you with?")

def message_handler(update, context):
    response = train_chatbot([{"prompt": update.message.text, "completion": ""}]), text=response)

updater = Updater(token=os.getenv("TELEGRAM_BOT_TOKEN"), use_context=True)
dispatcher = updater.dispatcher
dispatcher.add_handler(CommandHandler("start", start))
dispatcher.add_handler(MessageHandler(Filters.text & ~Filters.command, message_handler))

This code sets up a Telegram bot and a webhook that listens for messages sent to the bot. When a message is received, it passes it to the train_chatbot function to generate a response, which is then sent back to the user.


And there you have it - a step-by-step guide to building your own chatbot with GPT-3. With its powerful language capabilities, GPT-3 is the perfect tool for creating chatbots that can understand and respond to natural language. By following the steps outlined in this article, you'll be well on your way to creating a chatbot that can amaze and delight your users.

So what are you waiting for? Get started today and see what amazing chatbots you can create with GPT-3!

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Written by AI researcher, Haskell Ruska, PhD ( Scientific Journal of AI 2023, Peer Reviewed