All Comparisons
Techniques
RAG vs Fine-tuning
Retrieval-augmented generation vs. fine-tuning — when to ground a model on your data at query time versus baking knowledge into weights.
Feature
RAG
Fine-tuning
Uses fresh / live data
Low upfront cost
Source citations
Changes model behavior / tone
Works without retraining
Lower per-request latency
Easy to update knowledge
Handles narrow style / format
Supported Partial Not supported
Verdict
Use RAG when knowledge changes often, citations matter, or you want low setup cost. Fine-tune when you need consistent style, format, or behavior that prompting can't reach. They are complementary — many production systems use both.