Retrieval-Augmented Generation (RAG)

Retrieval-augmented generation is an approach where a language model, instead of relying only on what it learned during training, first retrieves relevant documents from an external source and then generates an answer grounded in that retrieved content.

This matters because it lets AI engines answer with current, verifiable information and cite their sources. When Perplexity or an AI Overview references a web page, RAG is the underlying process pulling that page into the answer.

In a RAG system, a query triggers a search across a corpus, often the live web, to find passages that best address the question. The model then synthesizes those passages into an answer and attributes the sources it used.

For content owners, this means citation depends on being retrievable and extractable. If your page is the clearest, most relevant passage for a sub-question and the model can access it, RAG is more likely to pull and cite it.

Because RAG retrieves passages, structure your content so individual passages stand alone as complete answers. Lead sections with direct answers, use clear headings, and keep information self-contained rather than scattered across the page.

Accessibility and authority also matter: ensure AI crawlers can reach your content, build topical depth, and keep pages current. The easier it is for a RAG system to retrieve and trust a passage, the more likely it is to ground an answer in your content.