RAG vs. fine-tuning: when to use which

RAG (retrieval-augmented generation) fetches relevant context at query time and passes it to the model. Fine-tuning adjusts model weights on your data. Both improve output quality—but for different reasons.

Use RAG when knowledge changes frequently. Product docs, policies, support tickets—content that updates often. RAG lets you add or change documents without retraining. Vector search + LLM is the standard pattern.

Use fine-tuning when you need consistent style, format, or domain terminology. If your outputs must follow a specific schema or tone, fine-tuning can help. It's more effort to maintain (retraining when data evolves) but can reduce prompt engineering.

Often you need both. RAG for knowledge retrieval, fine-tuning for output formatting or task-specific behavior. Start with RAG; add fine-tuning only if RAG alone is insufficient.

We help teams choose and implement RAG, fine-tuning, or hybrid approaches—with clear evaluation criteria and operational practices for each.