Which one is cheaper?

Retrieval costs more per request, because you send the fetched documents with every call. Fine-tuning costs more up front and then makes each request cheaper by shortening the prompt. At low volume retrieval wins easily; at very high volume on one narrow task, a fine-tuned small model can be the cheaper answer.

How do I keep answers current with each one?

With retrieval, you update the document and the next answer is current. With fine-tuning, current information means another training run, which is why nobody uses it for anything that changes weekly.

What should I try before either of them?

A better prompt with two or three examples, and a check that the model is actually failing at what you think. A large share of problems blamed on missing knowledge turn out to be a vague instruction or a retrieval step that returned the wrong passage.

Plain-language explainer

RAG or fine-tuning: how to actually choose

Should I use RAG or fine-tuning?

Ask what you are missing. If the model lacks facts, use retrieval: keep the documents outside the model and fetch the relevant ones into the prompt, so a wrong fact is an edit and answers can cite a source. If the model has the facts but gets the format, tone or task wrong, fine-tune: it buys consistency and shorter prompts. Knowledge is retrieval, behaviour is fine-tuning. Most teams need retrieval, and the ones who reach for fine-tuning first usually end up doing both.

Last reviewed July 25, 2026

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What people get wrong

Fine-tuning teaches the model your documents. It mostly teaches a style. Facts trained in this way are hard to update and impossible to cite.
RAG is the cheap option and fine-tuning is the serious one. Retrieval is what production systems run; fine-tuning is a specialisation on top.
It is one or the other. Fine-tune the behaviour, retrieve the facts. They solve different problems and compose.

Where you see it in real products

Support assistants that answer from a help centre and link the article they used.
Company assistants tuned to a house style, still fetching the live document.
Narrow classifiers fine-tuned for a fixed output format at high volume.

Common questions

Which one is cheaper?: Retrieval costs more per request, because you send the fetched documents with every call. Fine-tuning costs more up front and then makes each request cheaper by shortening the prompt. At low volume retrieval wins easily; at very high volume on one narrow task, a fine-tuned small model can be the cheaper answer.
How do I keep answers current with each one?: With retrieval, you update the document and the next answer is current. With fine-tuning, current information means another training run, which is why nobody uses it for anything that changes weekly.
What should I try before either of them?: A better prompt with two or three examples, and a check that the model is actually failing at what you think. A large share of problems blamed on missing knowledge turn out to be a vague instruction or a retrieval step that returned the wrong passage.

Related explainers

RAG Fine-tuning vs prompting Vector databases LLM evals

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