Glossary
GEO — generative engine optimization
GEO — generative engine optimization — is the practice of optimizing content for visibility in generative engines: AI systems that compose answers from retrieved sources. The term entered use through a 2023 research paper of the same name and remains the discipline's preferred name in academic work.
GEO and AEO describe the same territory from different angles — GEO from the research literature, AEO from the practitioner side — and the wire treats them as near-synonyms with different accents.
The research framing is worth knowing because it is unusually honest about the problem's shape: recent survey work describes GEO not as a ranking task but as a stochastic, partially observable pipeline spanning retrieval, citation, prominence, and user behavior. You are optimizing a system you can only see into partway, whose outputs vary between runs.
The literature also studies GEO's adversarial twin: the same techniques that win citations can be used to poison retrieved evidence, and benchmarks now test fact-checking systems against exactly that.
See also: AEOLLMEOAnswer engineRAG