RAG
RAG retrieves relevant documents first, then generates answers. Structured, retrievable content raises your chance of being cited in RAG systems.
Definition
RAG (Retrieval-Augmented Generation) retrieves relevant passages from documents or databases and then uses a model to generate an answer. To be useful in RAG, your content should be structured (headings, paragraphs, FAQs), machine-readable (knowledge.json/schema), and discoverable via stable URLs and sitemaps.
Why it matters
- RAG is a common architecture for AI search and assistants
- Retrievable, citable content is more likely to be used
- Extends SEO discovery into AI retrieval and citation
How to implement
- Use clear structure (H2/H3, FAQs, definitions) for chunking
- Publish machine-readable assets (knowledge.json, schema)
- Keep URLs stable, canonicals correct, and list them in sitemaps
Field notes / FAQ
FAQ
Common questions about this term.