AI

Fine-tuning

Further training a base model on your own examples to specialize its behavior.

Fine-tuning takes an existing model and nudges its weights with your data so it reliably adopts a style, format, or narrow skill — like teaching a fluent generalist your company's exact tone. It's powerful but often overkill: for 'answer from my documents,' RAG is usually cheaper and easier to keep current, since fine-tuning bakes knowledge in and must be redone when data changes. Reach for it when you need consistent behavior or output format that prompting alone can't nail — not to inject facts. It requires a dataset of example inputs and desired outputs.