Fine-Tuning
Fine-tuning is further training of an existing AI model on your own examples, so it consistently follows a style, format or classification scheme. It changes how the model behaves; it is not the best way to give it up-to-date company knowledge, which is what retrieval (RAG) is for.
Key Facts
| Good uses | Consistent tone or format, domain classification, extracting fields in a fixed schema |
|---|---|
| Data needed | Hundreds to thousands of high-quality input and output examples |
| Poor uses | Keeping answers current with changing documents or prices |
| Try first | Better prompts and RAG, which are cheaper to change |
Fine-tuning versus RAG
| Need | Better approach | | --- | --- | | Answers from current documents | RAG | | Consistent writing style | Fine-tuning or detailed prompts | | Classifying items into your categories | Fine-tuning, or prompts with examples | | Citing sources | RAG |
Before you fine-tune
Collect clean examples, define how you will measure improvement, and compare against a strong prompt-only baseline. See RAG or fine-tuning.
Frequently Asked Questions
Is fine-tuning expensive?
Training costs have fallen, but preparing good examples and evaluating results is the main effort.
Can I fine-tune a model to run on my own servers?
Yes, smaller open-weight models can be fine-tuned and run privately for narrow tasks.
Related Glossary
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