Both put your data to work inside a language model. Retrieval changes what the model is shown; fine-tuning changes how it behaves. Most enterprise problems are about the first, which is why retrieval is usually the first move.
| Criterion | RAG | Fine-tuning |
|---|---|---|
| Your data changes frequently | Update the store | Retrain the model |
| Cites its sources | Possible, and checkable | Not reliably |
| Respects per-user permissions | Enforced at retrieval time | Not reliably |
| Changes output style or format | Through prompting, within limits | Its strength |
| Latency | Adds a retrieval step | No extra step |
| Upfront cost | Lower | Usually higher |
| Evaluation burden | Retrieval quality plus answer quality | Regression pass on every retrain |
| Honouring a deletion request | Delete the record | Retrain without it |
For the large majority of enterprise use cases, RAG is the better first move, for three reasons that have nothing to do with model quality.
Your data changes. With retrieval, updating knowledge is updating a database. With fine-tuning, it is a retraining run and a regression pass, every time.
You need to know where an answer came from. Retrieval can return the document the answer was drawn from, which makes citation and traceability possible — the model can still misattribute, so citations are checked rather than trusted. A fine-tuned model cannot cite its sources reliably.
Permissions. The retrieval layer can enforce who may see what at query time, so a junior employee's question cannot surface board material. A fine-tuned model cannot un-learn data per user.
The working rule: retrieval for knowledge, fine-tuning for behaviour — and the two combine, with a fine-tuned model as the generation engine inside a retrieval architecture.
Yes, and for narrow high-volume work it is often the strongest arrangement. A smaller model fine-tuned on your domain's vocabulary and tone becomes the generation engine inside a retrieval architecture that supplies the current facts. Knowledge stays in the store; behaviour lives in the weights.
Fine-tuning usually costs more upfront and needs evaluation discipline most teams do not yet have: every retrain has to be regression-tested against a held-out set before it ships. Hosted fine-tuning APIs have made small fine-tunes cheap to run, so the cost that matters is the evaluation, not the compute. RAG is generally cheaper to build and maintain, though a high-volume retrieval system carries an ongoing vector-store cost.
No. It makes them attributable: an answer can be traced to what was retrieved, so a wrong one is diagnosable as a retrieval failure or a generation failure rather than a mystery. Evaluation makes the rate measurable and monitoring makes it visible. None of that is elimination, and anyone promising elimination is describing something that does not exist.
Bring the corpus and the question. We will tell you which one it needs — and it is usually the cheaper one.
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