Both let an LLM answer using knowledge it wasn't originally trained on — but they solve different problems, cost differently, and fail differently when you get it wrong. Most business use cases need RAG; fewer need fine-tuning than vendors want you to believe.
Start with RAG. It's cheaper, faster to iterate, and covers the vast majority of "answer using our data" use cases. Fine-tuning is a second step — worth considering once you know exactly what behavior RAG alone can't deliver, not a default starting point.
RAG retrieves relevant context at query time and passes it to the model as part of the prompt. Fine-tuning retrains the model's weights on your examples so the behavior is baked in permanently. RAG changes what the model knows in the moment; fine-tuning changes how the model behaves.
Usually, yes — for setup cost and especially for keeping answers current. Fine-tuning requires retraining every time your underlying information changes, while RAG just needs the knowledge source updated.
Yes. A common pattern is fine-tuning for tone, format, or specialized reasoning, combined with RAG for up-to-date factual grounding. It's more complex to build and maintain, so we only recommend it when a use case genuinely needs both.
When you need consistent behavior on every response — a specific format, tone, or specialized skill — that retrieval alone can't teach, and you have enough labeled examples to train on effectively.
Not sure which approach fits your data and budget? Tell us about your use case and we'll recommend a starting architecture.