An answer engine responds to a query with a composed answer rather than a list of links, drawing on retrieved sources and usually citing some of them. ChatGPT, Perplexity, and Google's AI Overviews and AI Mode are the prominent examples.

AEO — answer engine optimization — is the practice of making content visible, retrievable, and citable inside answer engines: the systems that answer questions directly instead of returning links. It spans machine-readable structure, entity clarity, question-shaped content, and crawler access policy.

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.

LLMEO — large language model engine optimization — is the same discipline as AEO and GEO under yet another name: making content that LLM-based systems retrieve, trust, and cite. It is the least standardized of the acronyms and is often folded into the other two.

SEO — search engine optimization — is the three-decade-old practice of earning visibility in ranked search results. It is the parent discipline of AEO and GEO, and its center of gravity is moving into them as search engines embed AI answer layers above the rankings SEO was built for.

AI crawlers are the bots AI companies run to gather web content — for model training, for building search indexes, or for live retrieval when answering a query. Major user-agents include GPTBot and OAI-SearchBot (OpenAI), ClaudeBot and Claude-SearchBot (Anthropic), Google-Extended (Google), and PerplexityBot (Perplexity).

Citation share is the fraction of an answer engine's citations that go to a given domain or brand across a set of queries — the emerging visibility metric of AI search, where being cited replaces being ranked.

RAG — retrieval-augmented generation — is the architecture behind most answer engines: retrieve documents relevant to the query, then have a language model compose its answer from them, grounding the response in sources it can cite.

A zero-click search ends without a click to any website: the query was answered on the results surface itself. AI answer layers intensify the pattern, which makes citation presence and brand visibility the remaining prize on those queries.