RAG (retrieval-augmented generation)
RAG is a technique where an AI system retrieves relevant documents at query time and feeds them to a language model, so the answer is grounded in real sources rather than memory alone.
RAG (retrieval-augmented generation) is a method where an AI system fetches relevant documents at the moment a question is asked, then hands those documents to a language model to write its answer. Instead of relying only on what the model memorized during training, RAG grounds the response in retrieved sources — which is exactly why RAG-based engines can cite real pages. For AI-SEO, RAG is the mechanism that turns a well-structured page into a quotable, citable source.
What does RAG mean?
RAG combines two steps: retrieval (find documents relevant to the question) and generation (use those documents to write an answer). The retrieval step pulls in fresh, specific content; the generation step synthesizes it into prose, often with citations back to the retrieved sources.
How is RAG different from a plain language model?
A plain model answers only from what it learned in training, which can be stale or generic. A RAG system adds a live lookup, so it can quote current, specific pages — and attribute them. For the deeper mechanics, read RAG explained: how retrieval chooses sources.
Example
A user asks an assistant, “What is the latest pricing for tool X?” A RAG pipeline retrieves tool X’s current pricing page, passes it to the model, and the model answers with the up-to-date figure and a link to the page. Without retrieval, the model might guess from outdated training data.