Fine-Tuning (vs RAG)

Fine-tuning further trains a model's weights on your own examples so it changes behavior — tone, format, a narrow skill — baked into the model itself, rather than supplying facts at query time the way RAG does. The two solve different problems: fine-tuning teaches a model how to respond, RAG gives it what to respond with. Reaching for fine-tuning to fix a knowledge or freshness problem that RAG (or better prompting) would solve cheaper and faster is a common build-vs-buy mistake.

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