When fine-tuning is worth it, when RAG is enough and when neither — a decision guide.
Category · AI & Agents
Trimming a model to the case.
Fine-tuning retrains a pretrained model on your own data so it performs better in a specific context — tone of voice, domain jargon, special classifications.
When fine-tuning, when RAG, when neither.
Rule of thumb: knowledge questions → RAG. Behaviour/tone/format → prompt engineering or fine-tuning. Regulated domain with recurring patterns that no longer fit in the prompt → fine-tuning.
Fine-tuning is the more expensive option, not always the better one.
See also
