AI
Fine-Tuning vs RAG: A Decision Guide
When to retrieve, when to fine-tune, and when you genuinely need both.
By Rajesh KumarJanuary 28, 20267 min read
"Should we fine-tune?" is usually the wrong first question. Start with what you're actually trying to fix.
Use RAG for knowledge
If the problem is the model not knowing your facts, retrieval is the answer. It's cheaper, updatable, and keeps answers grounded and citable.
Fine-tune for behavior
If the problem is format, tone, or a consistent task the model fumbles even with good context, fine-tuning shapes how it responds. It teaches style, not facts.
Often, both
Mature systems fine-tune for reliable behavior and retrieve for current knowledge. But reach for that only after good prompting and retrieval hit a measured wall.
