Fine-Tuning vs RAG

Also known as: FT vs RAG

The fine-tuning versus RAG decision determines whether to adapt a model by baking knowledge into its weights through training or by fetching knowledge at inference time. Fine-tuning is preferred for internalizing stable task formats, styles, and procedural behaviors that do not change frequently. RAG is preferred for accessing current, verifiable, or large-scale factual knowledge that would require retraining to update. Many production systems combine both: fine-tuning for behavioral shaping and RAG for factual grounding.