Retrieval-Augmented Generation (RAG)

Retrieval-augmented generation is the technique most AI answer engines use to ground their responses in real, current sources: before generating an answer, the system retrieves relevant documents (from the live web or a search index), then writes its response from that retrieved material and cites it. It's why a tool like Perplexity can answer about recent events and show you where each claim came from.

Why it matters to a founder doing AEO: RAG is the pipeline your citation travels through. The engine searches, picks a handful of trustworthy sources, and generates from them, so your job is to be one of the documents that gets retrieved and then quoted. That means being findable (indexed, crawlable, clearly about the topic) and being quotable (clear factual statements the model can lift without distortion). Understanding RAG demystifies AI visibility: you're not gaming a black box, you're making sure your content is both retrievable and worth retrieving when the model goes looking for an answer to your buyer's question.