RAG vs fine-tuning: a decision guide for business knowledge
Separate missing knowledge from inconsistent behavior, then test retrieval, permissions, citations, and abstention before training a model.
Sources checked · Analysis and proposed test protocols; not a vendor performance benchmark.
Should a business use RAG or fine-tuning for its knowledge base?
Use retrieval augmented generation when an answer needs current or private source material. Consider fine-tuning when the model needs more consistent behavior on a defined task. First test whether a clear prompt and supplied context solve the problem. Retrieval and tuning can be combined, but neither substitutes for access control or evaluation.
| Observed failure | First investigation | Evidence to keep |
|---|---|---|
| Outdated policy answer | Document freshness and retrieval filters | Policy version and retrieved passages |
| Correct facts, inconsistent output structure | Schema, examples, then task-specific tuning | Held-out examples and parser failures |
| Answer exposes another team's files | Authorization before retrieval | User identity and allowed document set |
| Confident answer with no supporting source | Retrieval relevance and abstention behavior | Expected source or explicit no-answer label |
Diagnose a missing fact before changing the model
Take a failed question and manually supply the correct policy paragraph. If the model now answers accurately, the first problem is likely finding and presenting evidence. Inspect ingestion, chunk boundaries, date filters, and the query itself. If the answer is still wrong with the exact source in view, investigate instruction clarity and task difficulty. This simple intervention prevents an expensive training project from becoming the default response to every failure.
Separate retrieval success from answer success
For each question, record the required source passage, whether it was retrieved, whether the answer used it correctly, and whether the citation actually supports the claim. A fluent answer can hide a retrieval miss. Include questions for which the library contains no answer and questions whose answer changed after a policy update. Keep the test questions separate from examples used to adjust prompts or train a model.
Test access boundaries as a release condition
Create two test identities with different document permissions and ask the same question. An answer for one identity must not reveal text, titles, or inferred facts from the other's restricted material. Re-run after changing access rights and deleting a document. The scorecard below records this as a blocking test even if ordinary answer accuracy is high. The checklist is an authored evaluation protocol, not a claim that any named product passed it.
Run a small, reproducible pilot
- Prepare 20 questions with expected sources, including five unanswerable cases.
- Compare a supplied-context baseline with your retrieval system using the same questions.
- Repeat under two permission profiles, then run the evaluation scorecard.
Sources and what they establish
- Microsoft Learn: retrieval augmented generation and indexes
RAG grounds answers in retrieved data; fine-tuning addresses behavior or task performance.
Continue with an implementation workflow
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