Retrieval-Augmented Generation (RAG)

RAG underpins most modern answer engines. When a user asks a question, the system searches a live index or knowledge base, pulls the most relevant passages, and feeds them to the language model as context for its answer. This lets engines cite current, specific sources instead of relying solely on what they learned in training.

RAG has direct implications for AI visibility. If your content cannot be retrieved, no amount of on-page quality will get you cited, because the model never sees it. Being retrievable means being crawlable, well-structured, and written so the most relevant chunk cleanly answers a likely question and is easy to quote.

DeepLexa's guidance treats retrievability as a prerequisite: its fixes help ensure AI crawlers can access your pages and that your passages are structured to be pulled into RAG-based answers.