RAG vs Fine-tuning | Boostora Ink

RAG supplies external information at answer time, while fine-tuning changes model behavior through additional training.

RAG and fine-tuning solve different problems. RAG retrieves information when a question is asked. Fine-tuning trains a model on examples so it behaves differently or follows a specialized style.

In practice

Use RAG when the model needs current, private, or large reference material. Use fine-tuning when the model needs a consistent format, tone, classification behavior, or repeated task pattern.

What to watch

Fine-tuning is not a good replacement for a changing knowledge base. If facts update often, retrieval is usually easier to maintain.