RAG vs Fine-Tuning: Which Strategy for Custom LLMs?
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 Feature RAG (Retrieval-Augmented Generation) Fine-Tuning Primary Use Adding knowledge Changing behavior Cost Low (Vector DB) High (GPU Training) Updates Real-time Requires retraining Hallucinations Reduced (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. ...