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AI glossary

Retrieval-Augmented Generation

Retrieval-augmented generation (RAG) is a technique that lets an AI look up relevant information first, then use what it found to give a more accurate, grounded answer.

What Retrieval-Augmented Generation means

A language model only knows what was in its training data, so it can be outdated or unaware of your private documents. RAG fixes this by fetching relevant information at the moment of the question and handing it to the model.

Picture an open-book exam: instead of answering purely from memory, the AI first searches a set of trusted documents - say, your company handbook - finds the relevant passages, and then writes an answer based on them.

Why Retrieval-Augmented Generation matters

RAG is how you build AI tools that answer reliably from your own information. It is a key technique behind genuinely useful business chatbots and assistants.

It lets AI answer from your own documents and data
It reduces made-up answers by grounding responses in real sources
It powers accurate support and knowledge chatbots
No-code tools increasingly make RAG simple to set up

Frequently asked questions

Asking directly relies only on the model's training, which may be outdated or unaware of your specific information. RAG supplies fresh, relevant sources, leading to more accurate and trustworthy answers.

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