RAG vs Fine-Tuning: When to Use Which for Your AI Product

The short version

Retrieval-Augmented Generation (RAG) and fine-tuning solve different problems. RAG injects fresh, external knowledge at query time. Fine-tuning changes how the model behaves.

Reach for RAG when

• Your knowledge changes often (docs, policies, catalogues).
• You need citations and traceability.
• You want to avoid retraining every time data changes.

Reach for fine-tuning when

• You need a consistent tone, format, or structured output.
• The task is narrow and well-defined.
• Latency and cost per call matter at scale.

In most real products we ship, the answer is RAG first, then a light fine-tune only if behaviour still drifts.