Direct answer
Almost always retrieval. Retrieval-augmented generation answers questions from documents you control, and when a document changes you update the document — no retraining. Fine-tuning changes how a model behaves, its tone, format and style, and it is a poor way to store facts, because facts change and a fine-tuned model has to be retrained when they do. Use retrieval for knowledge, fine-tuning only for behaviour.
They solve different problems
| Retrieval (RAG) | Fine-tuning | |
|---|---|---|
| What it changes | What the model has access to at answer time | How the model behaves |
| Good at | Answering from documents; citing sources | Tone, format, domain style, task shape |
| When a fact changes | Update the document | Retrain |
| Can it cite a source | Yes, the retrieved passage | No — the knowledge is in the weights |
| Cost of being wrong | Fix and re-index | Retrain and re-validate |
Why fine-tuning is the wrong tool for facts
Fine-tuning bakes information into model weights. That is a reasonable way to teach a format or a reasoning style. It is a bad way to store a price list, because the moment a price changes the model is wrong and the only remedy is retraining.
It also removes the citation. If the knowledge is in the weights, the model cannot point at the clause it came from — and for enterprise use, the citation is frequently the point. An answer a colleague cannot check is an answer a colleague will not rely on.
The practical combination
In most deployments the split is: retrieval for knowledge, prompt and configuration for behaviour. If your model's tone is wrong for customer-facing use, that is usually adjustable through instruction and examples before it warrants the cost of fine-tuning.
Consider fine-tuning when you have a stable, well-specified behavioural requirement and no amount of prompting achieves it — and when you can afford to retrain whenever the underlying material changes.
Key facts
| Default choice for a knowledge base | Retrieval (RAG) |
| Fine-tuning's proper use | Behaviour — tone, format, style, task shape |
| Why not fine-tune facts | Facts change; retraining is required each time, and citations are lost |
| Citation capability | Present in retrieval, absent in fine-tuning |
| Typical combination | Retrieval for knowledge, prompting for behaviour |
Sources
- Retrieval-augmented generation and fine-tuning practice
- Enterprise citation requirements for internal AI answers
Follow-up questions
Can I use both?
Yes, and some deployments do — retrieval for knowledge, light fine-tuning for a specific output format. Start with retrieval alone; it is cheaper to operate and easier to correct.
Will fine-tuning reduce wrong answers?
Not reliably, and it can make them harder to fix. Wrong answers in a knowledge base usually trace to retrieval returning the wrong passage. Fix the retrieval, and the wrong answers fall away with it.
Does retrieval mean I need a huge index?
No. A few dozen well-structured documents will carry a small business's most common questions. Start narrow and add as real gaps appear.