RAG vs fine-tuning
Retrieval or fine-tuning? Usually both, on evidence.
Retrieval keeps answers current and cited. Fine-tuning teaches a small model your tasks, formats and vocabulary. We measure both on your questions before deciding.
In short
Retrieval-augmented generation (RAG) answers from your documents at question time, so answers stay current and cite their source. Fine-tuning further trains a model on your examples so it learns tasks, formats and vocabulary. Facts that change belong in retrieval; repeated narrow tasks and strict latency budgets favour a fine-tuned small model. Many systems use both.
Side by side
What each is good at.
| Retrieval (RAG) | Fine-tuned small model | |
|---|---|---|
| Keeps facts current | Yes, no retraining | No, needs retraining |
| Cites sources | Yes | Not by itself |
| Learns formats and tasks | Partly, through prompts | Yes |
| Specialist vocabulary | Partly | Yes |
| Latency and hardware | Depends on the base model | Low: small models run fast |
| Respects permissions | Yes, at retrieval time | Only through what it was trained on |
How we decide
Measured, not assumed.
Build an evaluation set
Questions and tasks from your own work, with expected answers.
Measure retrieval alone
Usually the baseline, and often enough.
Try a fine-tuned small model
Only where the task is narrow, repeated or latency-bound.
Keep what wins
The evaluation set stays with you to re-test any change.
FAQ
Questions, answered.
Does fine-tuning leak our data?
Not with DeepVox: fine-tuning runs on your infrastructure and the weights are yours. Keep personal data out of training sets unless there is a clear basis.
Is fine-tuning expensive?
For small open-weight models, much less than it used to be. We size it in discovery.
Related
Fine-tuned small models
Your domain, your weights.
Learn more →Knowledge & retrieval
The core most systems start with.
Learn more →Glossary
RAG, SLM and more, explained.
Learn more →Start with two weeks of evidence, not a sales call.
A fixed-price discovery sprint, credited against whatever comes next. Or write to sales@deepvox.ai.