Glossary
Retrieval-Augmented Generation
Last reviewed: 2026-05-12
Retrieval-Augmented Generation (RAG) is an architecture in which the language model retrieves documents from an external index at inference time, then conditions its response on those documents. Most production AI search products are RAG-based.
Frequently asked
- What is Retrieval-Augmented Generation?
- Retrieval-Augmented Generation (RAG) is an architecture where the language model first retrieves relevant documents from an external index and then generates a response grounded in those documents.
- Why does RAG matter for AEO?
- RAG is the dominant architecture behind AI search products. Whether a brand appears in a RAG-generated response depends on whether its content was retrieved at query time.
- What signals make a page more retrievable?
- Clear topical focus, primary-source citations, freshness, schema.org markup, and inclusion in the engine's underlying index (typically Bing, Google, Brave, or Common Crawl).