Glossary
Retrieval-Augmented Generation (RAG)
A pattern where an AI model retrieves relevant documents at query time, then generates an answer grounded in those documents — the architecture behind most modern AI search engines.
Retrieval-Augmented Generation (RAG) combines two components: a retrieval system that finds documents relevant to the user's query, and a generation model that synthesizes an answer using those documents as context. Most modern AI search interfaces — Perplexity, ChatGPT with web search, Bing Copilot, Google AI Overviews — are RAG systems.
Why does RAG matter for GEO?
Being cited by an AI engine almost always means being retrieved first. If your page never enters the retrieval candidate set, it can't be cited regardless of content quality. GEO tactics that improve retrieval — schema markup, semantic clarity, fresh content, AI-crawler access — directly affect citation odds.
What does the retrieval step typically use?
A mix of keyword search (BM25 or similar), vector similarity over embedded passages, and live search APIs like Bing or Google. The exact mix varies per AI engine and per query type.
How do you optimize for RAG retrieval?
Write self-contained passages around named entities, include the query intent verbatim in headings, keep semantic chunks under ~250 words so they fit retrieval windows, and ensure schema markup labels the entity and its definition.
Part of the Cite Hustle GEO glossary — definitions for generative engine optimization and AI search.