Sources checked
What is the difference?
Retrieval adds selected source material to the context used for an answer. Fine-tuning changes model parameters through training. Neither method automatically makes every answer correct.
OpenAI’s retrieval documentation describes semantic search over your data. Its model optimization guide describes training on examples for particular tasks, while noting that its fine-tuning platform is winding down. Check current provider availability before planning an implementation.
| Problem | First approach to investigate | What to check |
|---|---|---|
| Answers omit a current policy | Retrieval from the policy library | Correct version and relevant passages |
| Answers use the wrong output format | Clear instructions and examples | Schema and required fields |
| Repeated task behavior remains inconsistent | Training options after a measured baseline | Held-out task performance |
| Facts and output style both matter | Retrieval plus task instructions or adaptation | Both evidence and behavior |
A support-policy example
Imagine a support assistant that must explain a return policy and produce three fields: eligibility, reason, and source. This is a hypothetical design example.
If it quotes an expired policy, first inspect the documents, version filters, and retrieved passages. Training on more old policy answers would not fix the source selection. If it finds the correct policy but omits the reason field, test clearer output instructions separately.
How should you decide?
Keep a small evaluation set with expected evidence and expected output. Change one part of the system at a time so you can tell what improved.
- Label failures as missing evidence, wrong evidence, or wrong behavior.
- Test better instructions with the evidence supplied directly.
- Test retrieval against the same questions.
- Investigate training only for a remaining, repeated behavior problem.
- Retest with examples excluded from development.
What are the trade-offs?
Retrieval needs maintained documents, access controls, and evaluation of the selected evidence. Fine-tuning needs suitable training examples, provider support, and evaluation on unseen inputs.
Avoid treating either as a shortcut around checking answers. A model can misread a retrieved passage, and training examples can encode a mistake.
Common questions
Does RAG train the model on my documents?
Ordinary RAG retrieves documents at answer time. It does not by itself update model weights. The provider’s storage and data-use policies remain a separate question.
Can I combine RAG and fine-tuning?
Yes. Retrieved evidence and adapted task behavior address different parts of the system. Introduce them only when evaluation shows a need.
What to remember
Diagnose whether the answer lacks the right evidence or the right behavior. Then choose the smallest change you can measure.
Sources & further reading
How this story was made
Written by Kristian Kostov with AI assistance and checked against the linked sources. Company performance claims are attributed to the company. Analysis reflects AiLookout’s interpretation; we have not independently tested the products discussed. Cover photography is illustrative and does not depict the specific announcement or product.
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