A clear, citable AI search glossary. This hub defines the core terms behind AI search optimization — from GEO, AEO and AI SEO to citations, grounding, share of voice and llms.txt — in plain language, so you can use the same words AI engines and the people building for them do. Each definition links to a deeper guide.
The AI search glossary is a reference set of plain-language definitions for the disciplines, engines, signals and technical foundations involved in getting a brand mentioned and cited by AI search engines such as ChatGPT, Perplexity, Gemini and Google AI Overviews.
TL;DR
What this is: a single, neutral reference for the vocabulary of AI search optimization — disciplines, engines, signals and technical foundations.
How to use it: each term has a tight, quotable definition and a “Learn more” link to a fuller guide.
Why it matters: AI engines reuse clear definitions; agreeing on the words is the first step to measuring and improving how you appear in them.
Why a shared vocabulary matters
AI search is young, and its terminology is still settling. The same idea gets called generative engine optimization, answer engine optimization or AI SEO depending on who is writing — and AI engines, which synthesize answers from many sources, reward content that defines concepts cleanly and consistently. This glossary collects the working definitions of AI search optimization in one place so teams, tools and the engines themselves can refer to the same meanings. The definitions below are written to be accurate and neutral; where a term overlaps with another, we say so rather than overstate the difference.
Generative engine optimization (GEO) is the practice of structuring and publishing content so that generative AI engines — like ChatGPT, Perplexity and Gemini — mention and cite your brand in the answers they synthesize. The term was introduced in academic work by Aggarwal and colleagues (KDD 2024). Learn more: Generative engine optimization →
Answer Engine Optimization (AEO)
Answer engine optimization (AEO) is the practice of structuring content so it can be selected as the direct answer to a question, whether that answer appears in an AI engine, a voice assistant or a search-result answer box. It emphasizes clear, self-contained responses to specific queries. Learn more: Answer engine optimization →
AI SEO
AI SEO is an umbrella term for adapting search optimization to AI-driven search — making content discoverable, parseable and citable by both traditional search engines and AI systems that summarize results. It bridges classic SEO and newer practices like GEO and AEO. Learn more: AI SEO →
AI visibility
AI visibility is the degree to which a brand is mentioned, cited and recommended by AI search engines when users ask questions in the brand’s category. It is the AI-era equivalent of search visibility, measured across answers rather than blue-link rankings. Learn more: AI visibility →
AI search optimization
AI search optimization is the broad effort to improve how a brand appears in AI-generated answers — combining the disciplines of GEO, AEO and AI SEO with measurement of mentions, citations and share of voice. It is concerned with being in the answer, not just ranking near it. Learn more: AI SEO →
Engines & surfaces
Answer engine
An answer engine is a search system that returns a single, direct response to a query instead of a list of links — for example a voice assistant or an answer box. AI answer engines build that response by synthesizing information from multiple sources. Learn more: Answer engine optimization →
Generative engine
A generative engine is an AI system that produces original, synthesized answers to queries using a large language model, often citing the sources it drew from. ChatGPT, Perplexity and Gemini are generative engines. Learn more: Generative engine optimization →
Google AI Overviews
Google AI Overviews is the AI-generated summary that can appear at the top of a Google search results page, synthesizing an answer from multiple web pages and linking to some of them as sources. It sits above the traditional blue links. Learn more: Google AI Overviews →
Signals & metrics
Citation (in AI answers)
A citation in an AI answer is an explicit reference — usually a link or named source — that attributes part of the answer to a specific website or document. Earning citations is a primary goal of GEO, because citations send signals of credibility and can drive referral traffic. Learn more: Track brand mentions in ChatGPT →
Brand mention vs citation
A brand mention is when an AI answer names your brand in its text; a citation is when the answer links to your site as a source. A brand can be mentioned without being cited, and cited without being prominently mentioned — so the two are measured separately. Learn more: Track brand mentions in ChatGPT →
Share of voice in AI
Share of voice in AI is the proportion of relevant AI answers in which your brand is mentioned or cited, compared with competitors, across a defined set of category prompts. It turns scattered mentions into a benchmarkable measure of presence. Learn more: AI visibility metrics →
Entity (in search/AI context)
An entity is a distinct, identifiable thing — a brand, person, product or concept — that search and AI systems recognize and connect to other entities in a knowledge graph. Clear, consistent entity signals help an AI system understand who you are and when to mention you. Learn more: AI SEO →
Prompt / query fan-out
Query fan-out is the technique where an AI search system expands one user query into several related sub-queries, retrieves results for each, and synthesizes them into a single answer. It means a brand can be surfaced through questions the user never typed directly. Learn more: Optimize for Google AI Mode →
Technical foundations
Grounding
Grounding is the practice of tying an AI model’s answer to verifiable external sources — such as retrieved web pages — rather than relying only on the model’s internal training. Grounded answers are more likely to cite the documents they were built from. Learn more: Generative engine optimization →
Retrieval-augmented generation (RAG)
Retrieval-augmented generation (RAG) is an architecture in which an AI system first retrieves relevant documents and then generates an answer based on them, instead of generating from training data alone. RAG is what lets generative engines cite live sources. Learn more: Generative engine optimization →
llms.txt
llms.txt is a proposed plain-text file placed at a website’s root that gives AI systems a curated, machine-readable map of the site’s most important content. It is intended to help large language models find and use a site’s key pages. Learn more: llms.txt generator →
Schema / structured data
Schema, or structured data, is standardized markup (commonly Schema.org) added to a web page so machines can reliably interpret its content — its entities, questions, answers and relationships. It helps both search engines and AI systems parse a page accurately. Learn more: Schema for AI Overviews →
Answer-first content
Answer-first content is content that states the direct answer to a question at the very top, before any background or context. This structure makes a passage easy for an AI engine to extract and cite as a self-contained response. Learn more: Answer engine optimization →
Featured snippet vs AI Overview
A featured snippet is a single passage Google quotes verbatim from one web page to answer a query; an AI Overview is an AI-generated summary that synthesizes and cites multiple sources. The snippet extracts; the overview composes. Learn more: Featured snippets vs AI Overviews →
How CapstonAI uses these concepts
These terms are not just vocabulary — they are the things CapstonAI measures. CapstonAI is a measurement and methodology platform that scans whether AI engines mention and cite your brand, separates mentions from citations, and benchmarks your share of voice against competitors across category prompts. Where most tools stop at reporting, CapstonAI connects measurement to action through agents for WordPress, Shopify, Drupal and Chrome, helping you fix the structural reasons — weak entity signals, missing schema, no llms.txt — that keep a brand uncited. We measure and help you improve; we are not an agency, and we don’t promise guaranteed citations or rankings.
Frequently asked questions
What is the difference between GEO, AEO and AI SEO?
GEO focuses on being mentioned and cited inside AI-generated answers; AEO focuses on being selected as the direct answer to a question across answer engines; AI SEO is the umbrella for adapting search optimization to AI-driven search. They overlap heavily and are often used interchangeably.
What is the difference between a brand mention and a citation?
A brand mention is when an AI answer names your brand in its text. A citation is when the answer links to your site as a source. A brand can be mentioned without being cited, so the two are measured separately.
What is AI visibility?
AI visibility is the degree to which a brand is mentioned, cited and recommended by AI search engines when users ask questions in the brand’s category. It is the AI-era equivalent of search visibility.
What is llms.txt?
llms.txt is a proposed plain-text file placed at a website’s root that gives AI systems a curated, machine-readable map of the site’s most important content, to help large language models find and use a site’s key pages.
What is a featured snippet versus an AI Overview?
A featured snippet is a single passage Google quotes verbatim from one web page; an AI Overview is an AI-generated summary that synthesizes and cites multiple sources. The snippet extracts; the overview composes.
Why do clear definitions matter for AI search?
AI engines synthesize answers from many sources and reward content that defines concepts cleanly and consistently. Clear, self-contained definitions are easier for an engine to extract and cite, which is the whole point of this glossary.
