Evidence-first authority page
RAG vs Fine-Tuning: Which Should You Use?
A practical framework for deciding between retrieval-augmented generation and fine-tuning to adapt a model to your specific data or domain.
Direct answer
Use RAG when your knowledge changes frequently or needs to be traceable to a source, and use fine-tuning when you need to change the model's style, format, or behavior pattern rather than its factual knowledge — the two solve different problems and are frequently combined rather than being mutually exclusive choices.
Last reviewed 2026-08-29
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When RAG is the better fit
- Your underlying knowledge base changes often (product catalogs, policies, documentation).
- You need to cite or trace an answer back to a specific source document.
- You want to avoid the cost and complexity of a training pipeline.
When fine-tuning is the better fit
- You need to change how the model responds (tone, format, following a specific style) rather than what facts it knows.
- The behavior you need is hard to specify through prompting alone, even with good examples.
- Latency matters enough that you can't afford a retrieval step at inference time.