Retrieval-Augmented Generation (RAG)
Retrieval-augmented generation (RAG) is a technique where an AI model fetches relevant external documents at query time and uses them to ground its answer, instead of relying only on what it memorized during training.
Many answer engines use RAG: when you ask a question, the system first retrieves fresh, relevant sources from the web or an index, then generates an answer grounded in those sources — which is why tools like Perplexity can cite up-to-date pages.
RAG is good news for businesses, because it means your current online presence (not just whatever was in the model's training data) can influence answers. Keeping your website, profile, and reviews accurate and crawlable directly affects what a RAG-based engine retrieves and cites about you.