Retrieval-Augmented Generation (RAG)
Retrieval-Augmented Generation (RAG) is a technique that lets an AI model look up relevant information from your own documents before answering, instead of relying only on what it was trained on.
How Retrieval-Augmented Generation works
An LLM's knowledge is frozen at training time and doesn't include your company's internal documents, policies, or product catalog. RAG solves this by searching a knowledge base (your documents, wiki, or database) for relevant snippets first, then handing those snippets to the model along with the question — so it answers using your actual, current information.
Why it matters for your business
RAG is what powers internal "ask our documentation" chatbots and customer support bots that answer using your actual product manuals, not generic internet knowledge. It's the standard approach for making AI answer accurately about your specific business.
Frequently Asked Questions
Why can't I just paste my documents into the chat every time?
You can for small, occasional tasks. RAG automates this at scale — automatically finding and feeding the model only the relevant snippets from potentially thousands of documents, for every query.
Does RAG stop AI hallucination completely?
It significantly reduces it by grounding answers in real source documents, but doesn't eliminate it entirely — good RAG systems also cite their sources so answers can be verified.