Retrieval-Augmented GenerationRAG
RAG is a technique where an AI model retrieves external documents at query time and grounds its answer in them, rather than relying only on training data.
Retrieval-Augmented Generation (RAG) is a technique where an AI system retrieves relevant external documents at query time and conditions its answer on them, instead of relying only on what the model memorized during training.
RAG is why GEO works: if a brand's content is in the retrieved set and is clear and authoritative, the model can ground its answer in it and cite it. Content that is well-structured, current, and extractable is more likely to be retrieved and used.
Key points
- ▸Retrieves external documents at query time
- ▸Grounds AI answers in real sources, enabling citation
- ▸Being in the retrieval set is the prerequisite to being cited