RAG vs Fine-Tuning: When to Use Each for Your AI Application

30-Second Explanation

RAG retrieves your knowledge at query time. Fine-tuning bakes behavior into the model. Most production apps need both.

Choose RAG When

• Your data changes frequently
• You need source citations
• You have limited training data
• You want to ship in two weeks

Choose Fine-Tuning When

• You need a specific output format
• You're operating at high volume
• You need domain-specific reasoning
• Latency matters

The Hybrid Approach

Fine-tune for style and structure. Use RAG for knowledge. That's the cheat code.