How should I structure training data for fine-tuning email replies in my style?

Below is some guidance based on the fine-tuning process described in the documentation:

Match Your Inference Setup

Your training examples should reflect what the model will see at inference time. In other words, if your production setup always includes your initial (standard) mail, then it makes sense to include that in your examples. This way, the model learns the full context of the conversation. On the other hand, if you want the model to focus solely on the customer's reply when generating your “perfect” response, then only include the customer’s message as the prompt.

Conversation Format

When preparing your dataset, each example should mimic a realistic conversation. For example, if your system message or earlier conversation turns provide key context, include all parts exactly as they appear in production. For instance:

JSONLCopied
{"messages": [
  {"role": "system", "content": "This conversation follows the business email style guidelines..."},
  {"role": "user", "content": "Initial mail: Hi there, my name is John. I’d love to discuss..."},
  {"role": "customer", "content": "Thanks for reaching out. I’m interested in learning more about your offer..."},
  {"role": "assistant", "content": "Hi, thanks for your reply. I’d be happy to provide further details..."}
]}

If your initial mail is always going out first and it helps shape the context of the conversation, include it. If it’s extraneous for crafting your response, you can use just the customer’s message as input and your reply as the completion.

Consistency with Formatting

If your real emails include greetings, headers, and signatures—and you want those as part of your answer style—then it’s important to include them in your examples. The model learns style and tone from the provided training data. Leaving out these elements in some examples might lead to unexpected variability in responses if, at inference time, the emails include such elements.

Summary Recommendations

• Include the Initial Mail if Relevant: If the initial mail is part of the conversation context seen by the model on production, add it to the training example.
• Structure Examples to Mirror Production: Whatever parts (such as greetings, headers, signatures) are used consistently in production emails should be present in the examples.
• Be Consistent: Consistency in instructions and formatting per training example is key to achieving the desired behavior.

By ensuring that your training data closely resembles the actual conversational inputs and outputs, you'll be well set up to fine-tune the model to respond as you would.