GEO & AI search glossary
Retrieval-augmented generation (RAG)
What is retrieval-augmented generation (RAG)?
The technique behind most AI search: before answering, the system retrieves relevant documents and writes its answer from them. GEO exists because the retrieval step decides which brands even get a chance to be mentioned.
A RAG pipeline has two gates. First retrieval selects a handful of documents; then generation writes the answer using only what it retrieved. Losing at the retrieval gate means you are invisible no matter how good the page is.
Optimizing for retrieval looks like clear topical scoping, one idea per section, unambiguous entity naming, and machine-readable structure. Optimizing for the generation step looks like extractable answers and quotable evidence. GEO has to do both.
Related terms
- Generative engineAn AI system that answers a question by writing new text rather than returning a list of links — ChatGPT, Claude, Gemini, Grok, DeepSeek and Meta AI are the six we optimize for.
- GroundingTying a model's answer to real, retrievable sources rather than to its training memory. Grounded answers are the ones that carry citations — and therefore the ones GEO can influence.
- Structured data (schema markup)Machine-readable JSON-LD embedded in a page that states what the page is about, who published it and how its entities relate. It does not force a citation, but it removes ambiguity about who you are.
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