What is RAG (Retrieval Augmented Generation)?
Updated May 2026
RAG is a pattern where the AI looks up relevant information first, then uses it to answer — like an open-book exam.
RAG combines retrieval (search through a document store, knowledge base, or database) with generation (the language model writes the answer using the retrieved context).
It's the dominant pattern for grounding AI in private data: customer support bots that read your docs, code assistants that read your codebase, business agents that read your real pricing.
Frequently asked questions
Is RAG better than fine-tuning?
For most business uses, yes. RAG is faster to update (change the docs, the agent reads the new docs), cheaper, and easier to debug than fine-tuning a model on your data.
Related terms
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