Retrieval-Augmented Generation (RAG)
Retrieval-Augmented Generation (RAG) is an AI architecture that combines a large language model with a retrieval system that fetches relevant documents from an external knowledge base or the live web before generating a response. Rather than relying solely on training data, a RAG system grounds its output in freshly retrieved content, reducing hallucinations and enabling up-to-date answers.
Most AI search engines — including Perplexity and Google's AI Overviews — use RAG architectures. For SEO, this means that being indexed, crawlable, and authoritative directly influences whether a site's content is retrieved and cited. Pages that are dense with factual, specific, well-structured information are more likely to be surfaced by a RAG retrieval step.
Why it matters for SEO
RAG is the mechanism by which AI search engines decide which websites to credit in their answers. Understanding RAG means recognizing that traditional crawlability and indexation remain prerequisites for AI visibility — well-structured, authoritative, and indexed content is what gets retrieved, cited, and ultimately shown to users.