# What is retrieval augmented generation?

> Retrieval augmented generation, or RAG, has a model look up relevant passages at answer time instead of relying only on its training.

Source: https://meshin.co/glossary/retrieval-augmented-generation
Updated: 2026-10-02

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## Why it comes up

Procurement asks "is this RAG, or did you train on our data," because the two have different data-handling implications. RAG typically sends only retrieved snippets to the model at query time, rather than baking a customer's content into the model's own weights. It also comes up once someone questions accuracy. A wrong answer usually traces to a retrieval problem, not a flaw in the model itself.

## What to do about it

Control access at the document-store level, not only at the chat interface. RAG will surface anything in the index to anyone who can ask the right question, regardless of who is meant to see it. Treat the document store itself as a data-classification boundary. Check what happens when a document containing hidden instructions ends up inside it. That exact scenario is one of the checks in the [90-Day Production Sprint](/services/90-day-production-sprint).