The debate between RAG and fine-tuning misses the point: they solve different problems.
When to use RAG
- Knowledge changes frequently
- You need citations and auditability
- Domain knowledge is large but well-documented
- Compliance requires traceable answers
When to fine-tune
- Model behavior needs to change (style, format, reasoning)
- Low-latency, high-throughput inference required
- Proprietary reasoning patterns not in base model
- You have high-quality training data (1k+ examples)
The pragmatic approach
Most production systems need both: RAG for knowledge, fine-tuning for behavior. Start with RAG, measure, then fine-tune only where retrieval alone fails.