Fine-Tuning: When to Use It
~10 min read
Fine-tuning is the right tool when you need to change the model's structure — its behavior, vocabulary, or writing style — rather than give it new knowledge. It changes HOW the model responds, not WHAT it knows.
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Key points
- •Fine-tuning changes the model's structure/behavior — vocabulary, style, reasoning patterns — not its knowledge
- •The book's example: internal meeting jargon needs consistent fluent handling, not a one-off lookup
- •Fits: consistent output format/tone across every response, domain-specific reasoning conventions, context too expensive to re-supply every prompt
- •Fine-tuning requires an actual training run — real compute cost, data curation, and time, unlike RAG's near-instant updates
- •Reaching for fine-tuning when the actual gap is missing knowledge (RAG's territory) pays this cost for the wrong problem