Questions

Should I use RAG or fine-tuning?

Short answer

Use RAG when the model needs to know your facts, documents or data, especially if they change. Use fine-tuning when you need to change how the model behaves, such as a consistent format, tone or a narrow task, and prompting is not enough. Most teams should start with prompting and RAG.

Last updated 2026-10-25

Choose RAG when

1Answers must come from your documents, products, policies or records.
2The information changes and must stay current.
3You need to show sources for each answer.
4Different users may see different data, based on permissions.

Choose fine-tuning when

1You need a consistent output format or style that prompts do not reliably give.
2A narrow, repeated task where a smaller tuned model can match a larger one at lower cost.
3You have enough good examples of the behaviour you want.

Start simple

Try a clear prompt with examples and structured output first, then add retrieval. Fine-tune only once evals show a gap that prompting and RAG cannot close. The two can also be combined: a tuned model that follows your format, fed with retrieved facts.

Tools

Check it for your case.

Frequently asked

Common follow-up questions.

Can fine-tuning teach the model our documents?

Not reliably. It changes behaviour more than it stores facts, and facts change. Use retrieval for knowledge.

Is RAG expensive?

It adds embedding, storage in a vector database and longer prompts. Usually much cheaper than retraining, and caching helps.

Does RAG stop hallucinations?

It reduces them by giving the model the right facts, but does not remove them. Check answers against sources and test with evals.

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