RAG vs fine-tuning
Use RAG when the knowledge changes or is proprietary, you update a document store, not the model. Use fine-tuning to teach a consistent style, format, or narrow skill. The strongest systems combine both.
Facts versus behavior
RAG changes what the model knows; fine-tuning changes how it behaves. If your problem is stale or missing facts, RAG solves it cheaply. If your problem is tone or format, fine-tuning solves it. Confusing the two wastes budget.
Teams often reach for fine-tuning to inject knowledge, then discover it is expensive to keep current and unreliable for facts. RAG avoids that entirely by keeping knowledge in an editable store. Reserve fine-tuning for the behavior problems it actually solves, a domain writing style, a strict output format, and let RAG carry the facts, which are the part that changes.
Frequently asked questions.
Is fine-tuning obsolete now that RAG exists?
No. Fine-tuning still shapes behavior, style, tone, structured output, that RAG cannot. The two are complementary: fine-tune for behavior, use RAG for the facts that keep changing.