---
title: "What Is RAG (Retrieval-Augmented Generation)? Definition"
description: "RAG is when an AI model retrieves relevant documents at answer time and grounds its response in them — why fresh, crawlable, quotable content earns citations."
canonical: https://aiovsseo.com/glossary/retrieval-augmented-generation.html
date: 2026-06-07
---
# What is Retrieval-augmented generation (RAG)?

Definition

Retrieval-augmented generation (RAG) is a technique where an AI model fetches relevant external documents at answer time and grounds its response in them, rather than relying only on what it memorized during training.

**Retrieval-augmented generation (RAG)** is how most answer engines stay current and grounded: when a query arrives, the system retrieves relevant documents (from the live web or an index), then generates an answer conditioned on that retrieved context — often citing it.

## Why it matters for optimization

RAG is the mechanism that makes [AEO](/articles/answer-engine-optimization-aeo.html) possible. Because the model fetches sources at answer time, fresh, crawlable, clearly structured content can be cited even if it was published after the model's training cutoff. Your `robots.txt` [Content-Signal](/glossary/content-signal.html) `ai-input` permission governs eligibility for this retrieval.

## Frequently asked questions

**How does RAG affect SEO?**

RAG means answer engines pull live content to ground responses, so freshness and crawlability directly affect whether you can be cited — even for very recent topics the model never trained on.

**Does RAG use my page even without training on it?**

Yes. RAG retrieves and reads your page at answer time independently of training. The ai-input signal in Content-Signal governs whether you allow this retrieval.