RAG
Retrieval-Augmented Generation — the technique behind most AI search citations.
Full definition
RAG (Retrieval-Augmented Generation) is the architecture that powers most AI search products. When you ask ChatGPT or Perplexity a question, the system first retrieves relevant web pages, then feeds them into the model to ground the answer. The 'retrieval' step is what decides which pages get cited.
Understanding RAG explains why GEO works: the retrieval step uses signals very similar to classical search (relevance, freshness, authority) plus answer-readiness signals (schema, paragraph structure). Optimizing for retrieval is what gets you cited.
Example
Perplexity's 'sources' list at the bottom of every answer is the retrieval step made visible.
Related terms
Generative Engine Optimization — optimizing for citation by AI chat, search, and assistants.
Numerical vectors that represent the meaning of text, used by AI to find semantically similar content.
Search powered by embedding similarity instead of keyword matching.
Google's AI-generated answers that appear above the classical search results.
Put it into practice
Run a free OptimAIze scan to see how your site handles RAG and the rest of the GEO checklist.
Run free scanFrequently asked questions
Is RAG the same as SEO?
No. RAG is one piece of the broader GEO (Generative Engine Optimization) program that sits on top of classical SEO. The two work together — classical SEO gets you crawled and indexed; RAG is part of what gets you cited by AI engines.
Do I need a tool to implement RAG?
For most teams, a free scanner like OptimAIze is enough to identify what's missing. Implementation is usually a copy-paste of generated markup or a small code change — no specialist tool required.