Q: RAG vs fine-tuning — how do I choose?

Default to RAG. Fine-tuning is the last resort, not the first move.

RAG is the right tool when the model needs knowledge it doesn't have: your documents, your data, anything current. It's cheaper, updatable in real time (change the documents, done), and auditable, because you can see exactly which sources fed each answer.

Fine-tuning is the right tool when the model needs a behavior it doesn't have: a strict output format, a specialized tone, a narrow task done thousands of times at low latency. It bakes patterns in, but the knowledge freezes at training time, and every update means retraining.

The test: if your problem is "the model doesn't know X," use RAG. If it's "the model doesn't act like Y," consider fine-tuning, and first check whether better prompting plus examples gets you there. It usually does.