RAG vs Fine-Tuning
Two primary approaches for making LLMs work with your proprietary data, compared across cost, accuracy, maintenance, and scalability.
| Feature | RAG | Fine-Tuning |
|---|---|---|
| Data Requirements | Document corpus for retrieval | Thousands of labeled examples |
| Cost | Lower upfront, vector DB + embeddings | Higher, GPU training costs |
| Update Frequency | Instant, re-index new documents | Slow, retrain model for new data |
| Accuracy on Domain | High with good retrieval + reranking | Very high for specific patterns/styles |
| Hallucination Risk | Lower, grounded in retrieved context | Higher, model may invent facts |
| Latency | Higher, retrieval + generation step | Lower, direct inference |
| Maintenance | Manage vector DB and chunking pipeline | Manage training pipeline and model versions |
When to use RAG
- ✓Knowledge bases that change frequently
- ✓Need citations and source attribution
- ✓Large document corpora (legal, medical, enterprise)
- ✓Budget-conscious projects with fast iteration
When to use Fine-Tuning
- ✓Consistent output format or style required
- ✓Domain-specific language patterns (medical coding, legal writing)
- ✓Low-latency inference is critical
- ✓Stable knowledge base that rarely changes
Verdict
Start with RAG for most enterprise use cases, it's faster to deploy, cheaper to maintain, and provides citations. Add fine-tuning when you need consistent output formatting, domain-specific language patterns, or when retrieval quality plateaus. The best production systems often combine both: fine-tuned models for generation quality plus RAG for factual grounding.
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