RAG vs Fine-Tuning: Which Strategy for Custom LLMs?

TL;DR

Don’t choose. Use RAG for knowledge (injecting facts) and Fine-Tuning for behavior (style, format, tone). Most production systems need RAG first.

Specifications Comparison

FeatureRAG (Retrieval-Augmented Generation)Fine-Tuning
Primary UseAdding knowledgeChanging behavior
CostLow (Vector DB)High (GPU Training)
UpdatesReal-timeRequires retraining
HallucinationsReduced (Grounded)Possible

RAG (Retrieval-Augmented Generation)

Pros

  • ✅ Up-to-date information
  • ✅ Traceable sources
  • ✅ Cheaper to implement

Cons

  • ❌ Context window limits
  • ❌ Retrieval latency
  • ❌ Complex architecture

Fine-Tuning

Pros

  • ✅ Perfect style matching
  • ✅ Lower latency (no retrieval)
  • ✅ Learn new tasks

Cons

  • ❌ Static knowledge
  • ❌ Catastrophic forgetting
  • ❌ Expensive compute

Verdict

Don’t choose. Use RAG for knowledge (injecting facts) and Fine-Tuning for behavior (style, format, tone). Most production systems need RAG first.

Last updated: February 2026