Fine-tuning vs. RAG
Fine-tuning vs. RAG · which one does your AI actually need?
Fine-tuning bakes new behaviour into the model's weights; RAG feeds the model fresh facts at query time. They solve different problems — and most teams reach for the wrong one first.
option AFine-tuningoption BRAGserviceAI solutions
→ Verdict
If the problem is knowledge — "the model should answer from our documents" — RAG wins almost always, and it's cheaper to run and update. Fine-tune when the problem is behaviour — a fixed format, a tone, a narrow classification — that prompting alone can't hold. Many production systems use both.
Pick a topic
When to pick which
A · Pick this when…
Fine-tuning
- 01You need a consistent output format or house tone prompting can't pin down
- 02It's a narrow, repeated task — classification, extraction, routing
- 03You want a smaller, cheaper model to match a bigger one on your task
- 04The knowledge is stable and rarely changes
B · Pick that when…
RAG
- 01The model must answer from your documents, policies or product data
- 02That knowledge changes — new docs, prices, tickets land all the time
- 03You need citations so an answer can be checked
- 04You want to add or remove a fact without retraining anything
Factors to weigh
Factor-by-factor
| Factors to weigh | Fine-tuning | RAG |
|---|---|---|
| What it changes | The model's weights — its learned behaviour | The context — what the model sees at query time |
| Updating a fact | Retrain or re-tune · slow and costly | Re-index one document · seconds |
| Citations | None · the model just 'knows' it | Built in · every answer can name its source |
| Upfront cost | A training run plus a labelled dataset | An embedding pipeline plus a vector store |
| Hallucination control | Indirect | Strong · answers are grounded in retrieved text |
| Best at | Behaviour, format, tone, narrow tasks | Knowledge, freshness, traceability |
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