"Should we use RAG or fine-tune?" is the most common question we hear. The short answer: they solve different problems. Retrieval-Augmented Generation (RAG) gives a model access to knowledge. Fine-tuning changes the model's behaviour.
What each one is good at
| RAG | Fine-tuning | |
|---|---|---|
| Best for | Facts that change, private documents, citations | Style, format, domain language, narrow skills |
| Updating knowledge | Re-index the documents | Re-train the model |
| Traceability | Can point to the source passage | Knowledge is hidden in weights |
| Upfront effort | Data pipeline, chunking, search quality | Curated training data, evaluation |
| Typical risk | Poor retrieval gives poor answers | Forgetting, overfitting, stale facts |
A simple decision flow
- Is the answer in your documents and does it change? Start with RAG.
- Does the model understand the task but answer in the wrong format, tone or language? Fine-tune, often with LoRA.
- Do you need both fresh facts and a specialised behaviour? Combine: fine-tune for behaviour, retrieve for knowledge.
- Is latency or cost the issue at scale? Fine-tune a smaller model so it can run locally.
RAG is mostly a search problem
Teams often blame the model when the real issue is retrieval. Invest in clean source documents, sensible chunking, hybrid search (keyword plus vector), re-ranking and metadata filters. For Indic languages, check that your embedding model actually supports them well.
Fine-tuning is mostly a data problem
A few thousand high-quality, diverse examples usually beat a huge noisy set. Hold out an evaluation set before you train, and compare against a strong prompted baseline. If prompting plus retrieval already reaches your quality bar, you may not need to train at all.
Measure what matters
For RAG, track retrieval hit rate, answer faithfulness to sources and refusal when sources are missing. For fine-tuning, track task accuracy, format compliance and regression on general abilities.
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