A training dataset supplies the examples used to fit a model’s parameters. For LLM fine-tuning, its quality and preparation strongly influence the result. Real, task-specific material can capture domain jargon, style, and typical questions—but only if you have the rights to use it and it covers the intended task. section 5.3.1 section 5.2.3
Prepare examples for the behavior the model needs to learn:
- Start with relevant records, such as human-agent conversations, FAQs, legal cases, or contracts. Favor accurate, domain-reviewed examples over a larger noisy collection. section 5.3.1
- Add human labels when the task requires them: issue-area tags for document classification, or ratings and annotations for response quality. section 5.3.1
- Correct false information and remove irrelevant or sensitive content. Check for biases that fine-tuning could amplify. section 5.3.1
- Cover the inputs and outputs expected in use: varied question phrasings, topics, target classes, and formats. Avoid overrepresenting a subcategory unless usage warrants it. section 5.3.1
Customer-support fine-tuning
Human-agent conversation logs can provide task-specific examples of issues and resolutions. Before using them, check rights, remove sensitive details, and make sure the issues represented resemble those the assistant will encounter. section 5.3.1
{"prompt": "Customer: <issue description>\nAgent:", "completion":
" <step-by-step resolution>\n"}
A prompt/completion record pairs a customer issue with an agent’s step-by-step resolution, making the intended response pattern concrete. section 5.3.1
A strong dataset does not eliminate the need for human review: even fine-tuned code assistants can produce boilerplate successfully while requiring careful review of complex logic. section 5.2.3
