Retrieval-augmented generation, or RAG, lets a language model pull outside documents at the moment of a question and build its answer from them rather than from training data alone. It matters for SEO because almost every AI surface you now compete in runs on it: AI Overviews, AI Mode, ChatGPT search, Perplexity, and Copilot all fetch live pages before they answer. This episode explains what RAG actually is and the two kinds of memory behind it, walks the four-stage pipeline of indexing, retrieval, reranking, and generation, shows why AI search engines are RAG systems that fan one query out into many, and where SEO fits inside the retrieval layer. It covers how embeddings and chunking change what you write, why the unit that gets cited is a passage and not a page, the on-page changes that make content retrievable, and the common mistakes SEOs mak ...