Retrieval-Augmented Generation is the architecture behind most AI search. Instead of answering purely from what a model memorised in training, a RAG system first retrieves relevant documents from a live index, then feeds them to the model to generate an answer grounded in what it just read, and usually cites those sources.
This is why your content can show up in an AI answer within days of publishing: it doesn't need to wait for the next training cycle, it just needs to be retrievable and clearly relevant. It's also why structure matters so much. Retrieval works on passages, so a clean, self-contained chunk that directly answers the question is far more likely to be pulled than the same point buried across a page.
For brands, RAG is the most influenceable layer of AI visibility: get reachable, get parseable, and you become eligible to be retrieved and cited.