RAG vs Fine-Tuning: When Should You Use Each?

This is one of the most misunderstood decisions in AI projects. The short version:

RAG changes what the model knows. Fine-tuning changes how the model behaves.

Use RAG When:

• Your information changes often (prices, policies, inventory).
• You need answers grounded in specific documents, with citations.
• Different users must see different data (permissions).
• You need to delete or update knowledge quickly, since removing a document from an index is easy and un-teaching a model is not.

Use Fine-Tuning When:

• You need a consistent tone, format, or style.
• You need the model to follow a complex structured output reliably.
• You have a narrow, repetitive task where a smaller tuned model can replace a bigger, costlier one.
• The behavior is hard to describe in a prompt but easy to show with examples.

Common Mistakes

• Fine-tuning to "teach the model our company knowledge." It's expensive, goes stale, and can't respect access control.
• Using RAG to fix a formatting problem that a better prompt or fine-tune would solve.

What most production systems do: start with prompting, add RAG for knowledge, and fine-tune only if a specific behavior gap remains. Many mature systems use both.

A quick test: if the answer to "what's wrong?" is "it doesn't know X," reach for RAG. If it's "it knows X but doesn't respond the way we want," think fine-tuning.

Have you tried fine-tuning? Was it worth it?