The two options
Option ARAG (retrieval-augmented generation)Grounds answers at query time by retrieving your documents into the prompt — the model's weights are untouched.
Option BFine-tuningAdjusts the model's weights by training on examples, baking new behaviour, format, or domain style into the model itself.
Side by side
RAG (retrieval-augmented generation) vs Fine-tuning, dimension by dimension
| Dimension | RAG (retrieval-augmented generation) | Fine-tuning |
|---|---|---|
| What it changes | Adds knowledge at query time by retrieving relevant documents into the prompt. The model itself is unchanged. | Changes the model's weights by training on examples, so new behaviour, format, or style is built in. |
| Best for | Grounding answers in specific, private, or fast-changing facts — knowledge bases, documentation, policies, records. | Teaching a consistent output format, a domain tone, or a narrow task the base model handles clumsily. |
| Keeping knowledge current | Re-index the documents and the system is up to date — no retraining required. | New facts need a fresh training run; between runs the model's knowledge goes stale. |
| Traceability | Answers can cite the exact passages they were drawn from, which matters in regulated settings. | Knowledge is diffused into the weights — there is no source to point back to. |
| Cost and effort | Mostly engineering: chunking, embeddings, retrieval tuning, and an index to operate. | Data preparation plus training compute, and recurring cost to re-train as the data shifts. |
| Effect on hallucination | Lower when retrieval is good; the failure mode is confidently answering when the right context wasn't found. | Improves format and adherence, but does not ground facts — the model can still invent confidently. |
The honest verdict
When each one wins
For almost any "make the model know our data" problem, start with RAG: it is cheaper, keeps knowledge current, and lets you cite sources. Reach for fine-tuning when the issue is behaviour rather than knowledge — a consistent output format, a domain voice, a narrow task the base model does poorly, or cost and latency pressure that favours a smaller specialised model. The two are not mutually exclusive: many production systems fine-tune for format and tone while using RAG for facts. The costly mistake is fine-tuning to teach facts that change, then watching the model quietly drift out of date.