AEO / Glossary / Retrieval-Augmented Generation (RAG)
AEO Glossary
Retrieval-Augmented Generation (RAG) is an AI architecture that combines a language model with a retrieval system. When a user asks a question, the system first retrieves relevant documents from a knowledge base or the live web, then feeds those documents to the language model to generate a cited, grounded answer. Perplexity AI, Bing Copilot, and ChatGPT with web browsing all use RAG.
Understanding RAG explains why AEO works: RAG systems will only cite content they can retrieve. Your brand needs to be (1) crawlable by AI crawlers, (2) present in the retrieved documents, and (3) accurately described so the model can quote it correctly. The quality and structure of your content directly determines whether it gets retrieved and cited in RAG-powered answers.
Optimize for RAG by ensuring your content is: structured with clear headings (so retrieval models can identify relevant chunks), factually dense (specific data points are easier to retrieve), crawlable by AI bots, published on HTTPS, and fast-loading. Add Organization and FAQ schema so the retrieval index can correctly classify what kind of content it's reading.
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