How to train ChatGPT on custom datasets

Imagine yourself as a sculptor with a block of marble in front of you. That marble is ChatGPT—a powerful, pretrained language model brimming with potential but unrefined. Your task? To carefully sculpt it into something uniquely yours. Training ChatGPT on custom datasets is just like this: an artistic and technical endeavor, transforming a general-purpose language model into a finely tuned tool tailored to your specific needs.

The path requires patience, precision, and a touch of creativity. Let’s embark on this journey of fine-tuning ChatGPT step by step, much like guiding an apprentice through the intricacies of a workshop.

Key takeaways:

• Training ChatGPT on custom datasets is likened to sculpting—transforming a pretrained language model into a finely tuned tool for specific tasks. The process requires patience, precision, and creativity.

• Fine-tuning involves refining an already trained model (ChatGPT) with custom data to sharpen its skills for a specific use case. The model adapts to task-specific demands without starting from scratch, much like adding purposeful details to a sculpture.

• A well-prepared dataset is key to fine-tuning success. Like a sculptor selecting the right marble, the data should be curated with task-specific prompt-completion pairs that guide the model's learning. High-quality, relevant data leads to better outcomes.

• Fine-tuning isn’t the only method for customizing ChatGPT. In-context learning allows the model to adapt on the fly using provided examples during a session, while retrieval-augmented generation (RAG) fetches external information dynamically to generate responses, minimizing the need for fine-tuning.

Method 1: fine-tuning

Fine-tuning is akin to giving that marble a purpose. It involves taking a pretrained AI model like ChatGPT and refining it with custom data for a specific task. You’re not starting from scratch—ChatGPT has already been trained on vast amounts of text. Instead, you’re sharpening its skills, molding it for your unique use case.

As Jim Rohn said, “Success is nothing more than a few simple disciplines, practiced every day.” Fine-tuning reflects this mindset—a methodical process of feeding task-specific data to help ChatGPT excel in exactly what you need.

Step 1: Installing the OpenAI CLI

Before chiseling begins, you need your tools in place. To start fine-tuning, you’ll need to install the OpenAI command-line interface (CLI). You can do this by running the following command in your terminal:

Next, you’ll need to set your OpenAI API key as an environment variable. You can do this by adding the following line to your shell initialization script or running it in your command line:

Note: Replace <OPENAI_API_KEY> with your API key.

This is the gateway to fine-tuning your custom AI model.

Step 2: Preparing your training data

Now that the tools are ready, it’s time to gather the marble—your training dataset. The training data serves as the foundation, shaping how ChatGPT responds to specific tasks. It’s essential to ensure this data is well-prepared, as it’s the core of the fine-tuning process.

Suppose that you are aiming to automate customer support. You prepare your custom dataset in JSONL format, where each line contains a prompt-completion pair. This helps ChatGPT understand and handle customer queries fluently:

Note: You can use the OpenAI CLI data preparation tool to convert your data into this format.

As W. Edwards Deming once said, “Without data, you’re just another person with an opinion.” High-quality data breathes life into ChatGPT, giving it the context and relevance it needs to perform.

Step 3: Creating a fine-tuned model

Once your data is ready, you can start the fine-tuning process. Use the OpenAI CLI to create a fine-tuned model with the following command:

This command uploads your training file, creates a fine-tuning job, and streams events until the job is complete.

Step 4: Using your fine-tuned model

After your fine-tuning job is successful, you can use your new model with the OpenAI API. The fine_tuned_model field will be populated with the name of your model, which you can use as a parameter in the Completions API.

For example, if your model’s ID is my_model, you can use it to generate completions like this:

• Line 1: import openai imports the OpenAI library.

• Line 3: openai.Completion.create( calls the create method of the Completion class.

• Line 4: model="my_model" specifies the model to use, in this case, "my_model".

• Line 5: prompt="My internet is not working. What should I do?" sets the prompt for the model to respond to.

• Line 6: max_tokens=60 limits the response length to 60 tokens.

Step 5: Polishing the details

No masterpiece is perfect after the first pass. The real power of fine-tuning ChatGPT lies in the ability to iterate—adjusting the training dataset or adding more examples to refine your model’s responses further.

As Michelangelo famously said, “Every block of stone has a statue inside it, and it is the task of the sculptor to discover it.” The same is true with fine-tuning AI models—you refine your model through repeated learning, gradually perfecting it.

Alternative methods: In-context learning and RAG

While fine-tuning offers great control, it’s not the only way to adapt ChatGPT for custom tasks. Imagine you have to mold a sculpture repeatedly for slightly different clients—sometimes, you may not need to chisel the marble again but simply adjust how you display it.

This is where in-context learning and retrieval-augmented generation (RAG) come in. These methods offer more flexibility when you don’t want to fine-tune the model entirely.

Method 2: In-context learning

Instead of retraining ChatGPT, in-context learning allows you to provide a few examples during the interaction. The model adapts its responses based on these examples, learning “on the fly” without needing a dedicated fine-tuning process. This is particularly useful when the task changes frequently or if you don’t have enough data for a full fine-tuning session.

For example, you can prompt ChatGPT with a few customer support examples, and it can learn how to respond based on those without being retrained:

This way, the model “learns” the task during the session itself.

Method 3: Retrieval-augmented generation (RAG)

RAG, on the other hand, integrates an external retrieval system with the language model. Imagine needing to reference a massive encyclopedia while sculpting to ensure your details are always accurate. RAG allows ChatGPT to dynamically fetch information from a knowledge base as part of its response generation.

For example, if you’re building an AI for technical support, RAG could pull real-time information from documentation or databases:

With RAG, you don’t need to include every possible piece of information in the training dataset. Instead, the model retrieves what it needs, significantly reducing the need for constant fine-tuning.

Conclusion

Training ChatGPT on custom datasets is both a technical and creative process. It allows you to sculpt an AI model that understands your specific tasks with precision. With careful preparation, thoughtful fine-tuning, and continuous iteration, you can craft a tool that serves your needs in ways no out-of-the-box solution can.

But fine-tuning isn’t always the only option. In-context learning and retrieval-augmented generation (RAG) offer alternative, flexible methods for shaping ChatGPT without the overhead of full retraining. Depending on your needs, you can decide whether to chisel away with fine-tuning or dynamically adjust with RAG and in-context learning.