What Is RAG? Definition for AI Search
A technique where AI systems first retrieve relevant sources and then generate an answer from them.
What is RAG?
Retrieval-augmented generation (RAG) is an AI technique in which a language model first retrieves relevant documents from a knowledge base or the web (retrieval) and then uses them to generate an answer (generation).
Example: a user asks an AI assistant which documents are needed to register a company. Before answering, the system searches the web, picks a few pages that cover the topic, and writes its answer based on those pages, often with links to them as sources.
Why does RAG matter for GEO?
RAG is the mechanism that makes LLMO and GEO possible in the first place. When ChatGPT, Perplexity or Google AI Overviews generate an answer, they first look for relevant sources. Your content has to be optimized so that it is found in this retrieval step and rated as trustworthy.
- Structured data – makes content easier for machines to understand
- Clear facts – quotable statements are preferred
- Authority – E-E-A-T signals influence which sources are chosen
Our article on how AI search systems select sources explains the retrieval step in more detail. To find out whether your content is picked up, see our GEO/LLMO audit.
Frequently asked questions
What is RAG (retrieval-augmented generation)?
RAG is an AI technique in which a language model first retrieves relevant documents from a knowledge base or the web and then uses them to generate an answer.
Why does RAG matter for SEO?
RAG is the mechanism behind LLMO and GEO. AI search systems look for sources before they answer, so your content has to be found in this retrieval step and rated as trustworthy.
What is an example of RAG?
A user asks an AI assistant how to register a company. The system first searches the web, selects a few relevant pages and writes its answer based on them, often citing them as sources.