Retrieval-Augmented Generation (RAG) is a method in which a language model dynamically retrieves relevant documents or passages at inference time and then uses them to generate more accurate, contextually grounded responses. Unlike purely generative approaches, RAG grounds LLM outputs in verifiable sources. From an AI SEO perspective, ensuring your site’s content is formatted and indexed in ways that RAG-enabled platforms can easily fetch (e.g., clean HTML, clear headings, robust metadata) increases the likelihood your pages are retrieved and cited in AI-driven answers.