Canonical AI knowledge page

RAG vs Fine-Tuning

Use RAG when answers need fresh or private knowledge; use fine-tuning when the model needs a learned behavior, domain style, format, or repeated task pattern.

LLM Summary

Use RAG when answers need fresh or private knowledge; use fine-tuning when the model needs a learned behavior, domain style, format, or repeated task pattern.

Canonical: https://www.aiwisdom.dev/rag-vs-fine-tuning

Choose RAG for knowledge

RAG retrieves documents at query time and grounds the model in sources. It is best for current data, enterprise documents, citations, policies, product catalogs, and fast-changing facts.

Choose fine-tuning for behavior

Fine-tuning changes model behavior through examples. It is best for style, classification boundaries, structured task habits, domain-specific formats, and repeated patterns that prompts cannot reliably enforce.