AI Glossary
What is fine-tuning?
Fine-tuning: Fine-tuning takes an already-trained model and trains it further on your own examples, so it adopts your task, tone or domain by default.
In plain English
Pretraining hires a brilliantly well-read generalist; fine-tuning is onboarding them with your company's style guide and a stack of past work marked "do it like this". After enough examples, you stop having to explain — the behavior becomes the default.
Why it matters
- Best for changing behavior (format, tone, specialty) — not for adding facts
- For fresh or private knowledge, RAG usually beats fine-tuning
- Much cheaper than training from scratch, but needs hundreds of good examples
A concrete example
A clinic fine-tunes a model on 2,000 of its own discharge summaries; the model then writes new ones in exactly the house format without being told each time.
Related terms
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