Choose RAG when the facts are the problem
- Your documents change often.
- The answer must cite a policy, manual, or source.
- The information is private or specific to your organization.
- You want a content update to take effect without retraining a model.
Consider fine-tuning when behavior is the problem
- The model has the necessary information but follows the format inconsistently.
- You have a durable set of high-quality examples.
- The task is classification, style, or a repeatable transformation.
- You can run evaluations after each model or dataset change.
A real decision example
Suppose you want a support assistant to answer from a refund policy that changes every quarter. Fine-tuning is the wrong first move: the policy will go stale and you will need a new training cycle. RAG lets you replace the policy document and test the same questions again. If the assistant still cannot return the answer in your required JSON format, then behavior tuning may be worth testing.
Use both only when each solves a different problem
Fine-tuning can teach a stable response pattern while RAG supplies changing facts. Keep those responsibilities separate and evaluate them separately. Otherwise, a bad answer becomes difficult to diagnose: was the source missing, or did the tuned behavior distort it?
Decision rule
| Your main problem | Start with | Why |
|---|---|---|
| The model does not know current company information | RAG | The knowledge belongs in a retrievable source |
| The answer needs current citations | RAG | The source can be replaced and inspected |
| The output format is inconsistent | Prompting and evals | Measure the failure before training |
| A stable style or classification task fails repeatedly | Fine-tuning | The behavior may benefit from examples |
| Both changing facts and stable behavior matter | RAG plus fine-tuning | Each layer has a separate job |