GUIDE
RAG vs fine-tuning
These solve different problems. RAG gives a model your knowledge at answer time. Fine-tuning changes how the model behaves. Most business needs are a knowledge problem, not a behavior problem.
Last updated October 2026.
| Dimension | RAG | Fine-tuning |
|---|---|---|
| What it changes | The information available at answer time. | How the model behaves and responds. |
| Best for | Questions answered from your documents and data. | Tone, format, and a narrow task done one way. |
| Updates | Change a document and the answer changes. | Requires retraining to take effect. |
| Cost | Mostly setup and retrieval. Cheaper to keep current. | Needs data, training runs, and re-runs when things change. |
| Risk | Retrieval quality decides accuracy. | Overfits or drifts without careful evaluation. |
When to choose RAG
When the answer depends on information the model did not learn: your policies, your documents, your data. RAG keeps answers current without retraining, and it can point to the source. This covers most business use cases.
When to choose fine-tuning
When the problem is behavior, not knowledge: a specific output format, a narrow task, or a consistent voice, and you have enough good examples to train on. It is rarely the first move, and it is expensive to keep current.
They combine
A system can retrieve your knowledge and use a tuned model for tone or format. Start with RAG, measure, and add tuning only if a real gap remains. Related: RAG development and what is RAG.
Knowledge problem or behavior problem?
Tell me what the system gets wrong. I will tell you whether it is retrieval, tuning, or neither.
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