RAG vs fine-tuning: which does your AI product need?

Teams often reach for fine-tuning when they actually need retrieval. The two solve different problems, and choosing wrong is expensive. Here is the simple rule of thumb.

Use RAG when you need knowledge

Retrieval-augmented generation grounds the model in your data at query time. Use it when the model needs to know facts — your docs, products, policies — especially when that information changes. It is cheaper, faster to ship, and easy to update (just change the data).

Use fine-tuning when you need behaviour

Fine-tuning teaches the model a style, format or skill — a consistent tone, a structured output, a narrow classification task. It does not reliably teach new facts, and it must be redone when your data changes.

A simple decision guide

• Need current, changing facts? → RAG.
• Need a consistent voice or output format? → Fine-tuning.
• Need both? → RAG for knowledge, light fine-tuning for behaviour.
• Not sure? → Start with RAG. It solves most product needs at lower cost and risk.

Whichever you choose, the unglamorous work — evaluation suites, guardrails and human review — is what makes the feature trustworthy in production.