AEO Glossary: Key Terms and AI Visibility Metrics Explained
- Jul 3
- 6 min read
Answer engine optimisation, or AEO, is the practice of structuring content so large language models, or LLMs, can find it, understand it and cite it in a generated answer. This AEO glossary collects the core terms behind that practice, alongside the specific metrics used to measure whether a brand actually shows up in AI-generated answers.
Sources used throughout: Pew Research Center on how AI summaries affect click behaviour (July 2025); the Princeton/Georgia Tech/IIT Delhi paper that introduced the term GEO, presented at KDD 2024.
Core AEO and GEO terms
AEO (Answer Engine Optimisation) Structuring content so it can be selected and cited as a direct answer by an LLM rather than simply ranked in a list of links. Read the full guide to answer engine optimisation for a deeper walkthrough.
GEO (Generative Engine Optimisation) A broader term covering visibility across any generative AI system, not only chat-style answer engines. The term was formally introduced in a 2023 paper from Princeton, Georgia Tech and IIT Delhi, which found that structuring content correctly can boost visibility in AI-generated answers by up to 40%.
AI SEO An umbrella term used interchangeably with AEO and GEO, emphasising that this is still search optimisation at its core, just aimed at AI-generated answers instead of a ranked list of links.
LLMO (Large Language Model Optimisation) A third, less common label for the same discipline, used mostly in academic and technical writing rather than marketing contexts.
LLM (Large Language Model) The technology behind conversational AI tools such as ChatGPT, Claude and Gemini. LLMs generate answers by combining what they learned during training with information retrieved at the moment of the query.
Answer engine Any AI product that responds to a query with a direct, generated answer instead of a ranked list of links. Google's AI Overviews, ChatGPT and Perplexity all function as answer engines.
Generative engine The wider category of AI systems that produce original text in response to a query, including answer engines and broader conversational assistants.
AI Overview Google's AI-generated summary that appears above standard search results for a share of queries. Pew Research Center found that Google users click a traditional result link just 8% of the time when an AI Overview appears, compared with 15% when it does not.
SGE (Search Generative Experience) The original name for Google's AI Overviews feature during its early testing phase. The functionality is the same, only the name changed once it rolled out to all users.
Citation The moment an LLM references a specific brand, page or source inside its generated answer. A citation is the AI-search equivalent of a backlink.
Zero-click search A search that ends without the user clicking through to any website because the answer engine already supplied the answer. Zero-click behaviour is now common enough that being cited inside the answer matters more than ranking below it.
Structured data (schema markup) Code added to a web page that describes its content in a machine-readable format, helping AI systems parse what a page is actually about.
FAQPage schema A specific type of structured data marking up question and answer content, a direct signal that helps LLMs extract short answers for citation.
Grounding The process by which an LLM anchors its answer in retrieved source material rather than generating purely from memory. Well-grounded answers cite real sources and hallucinate less.
Hallucination When an LLM generates information that is false or unsupported by any retrieved source. Structured, well-sourced content reduces the likelihood that a model will hallucinate about a brand.
RAG (Retrieval-Augmented Generation) The technique combining a retrieval step with text generation, letting an LLM pull in real documents before writing an answer rather than relying only on what it learned during training.
Chunking Splitting long text into smaller passages before it is indexed for retrieval, improving how precisely an LLM can pull the exact relevant section rather than an entire page.
Passage-level answerability How well a single self-contained section of a page can serve as a complete answer on its own, without needing the rest of the page for context.
E-E-A-T Shorthand for experience, expertise, authoritativeness and trustworthiness, a framework LLMs weight heavily when deciding which sources to trust and cite.
Share of Voice The proportion of all brand mentions in a set of AI answers that belong to a specific brand rather than its competitors. A brand with a 30% share of voice is mentioned in roughly one of every three brand references LLMs generate on that topic.
Entity A specific, uniquely identifiable person, brand, place or concept that an AI system can recognise and distinguish from similarly named entities.
Knowledge Graph A structured database of facts and the relationships between them, used by AI systems to verify and enrich answers about brands and topics they already recognise as distinct entities.
Topical authority The degree to which a website is seen as a comprehensive, trustworthy source across an entire subject area rather than a single page ranking for one keyword.
Content cluster (pillar page) A broad hub page covering a topic from every angle, linking out to narrower supporting pages. LLMs read this structure as a signal of depth and organisation rather than a scattered set of unrelated posts.
People Also Ask (PAA) A Google search feature listing related questions users commonly ask. It is a useful source of real, high-probability question phrasing for FAQ sections.
Featured snippet A single highlighted result at the top of Google's search results, pulled directly from one source page. It predates AI Overviews but works on a similar extraction principle.
Golden answer A clear, factual, well-structured answer to a specific query that AI engines consistently select and cite over competing sources.
Query fan-out The process by which a single user prompt triggers several background searches inside an LLM before it composes one combined answer.
Direct answer format Writing that states the core answer in the first one to two sentences before adding supporting detail, matching how LLMs weight the opening of a passage most heavily when extracting an answer.
Canonical tag An HTML tag signalling which version of a page is the authoritative one when similar or duplicate content exists at multiple URLs.
Crawl budget The number of pages a bot is willing and able to crawl on a given site within a given time. Wasted budget on duplicate or low-value URLs can mean AI crawlers miss important pages entirely.
Voice search Spoken queries made through voice assistants, which tend to be longer and more conversational than typed searches and often trigger the same answer-engine mechanics as chat-based AI queries.
SXO (Search Experience Optimisation) The combination of strong answer content with a fast, clear, well-designed page. A great answer on a slow or confusing page still underperforms, because AI systems and users both weight the full experience.
AI visibility metrics explained
These are the exact terms AI, TELL ME! uses in its AEO Monitoring reports and GEO Report audits.
Mention Rate How often a brand appears at all across a set of AI-generated answers to relevant prompts. A mention rate of 40% means the brand showed up in four out of every ten tested prompts.
Average Rank Where a brand tends to land when an AI answer lists brands in order, averaged across every prompt where it appeared at all. Lower is better, first position scores as rank 1.
First Mention Rate How often a brand is the very first one named in an AI answer, not just present somewhere in the list. This is a stricter, more competitive metric than mention rate alone.
Missed Prompts The share of tested prompts where a brand did not appear at all. This is effectively the inverse of mention rate and highlights the specific gaps worth investigating first.
GEO Score A single composite score summarising how visible a brand is across AI-generated answers, built from mention frequency, share of voice, sentiment and ranking position combined into one number.
Citable Block Density A technical measure of how much of a page's content is structured in a form LLMs can lift directly, such as short lists, tables and clearly stated facts, rather than long unstructured paragraphs.
Extractability How easily an LLM can pull a clean, self-contained answer from a page without needing to trim or interpret it. Short, atomic paragraphs and list items score higher than dense prose.
FAQ
Is there a real difference between AEO and GEO, or are they the same thing? In practice, most teams use them interchangeably. AEO originally emphasised answer engines specifically, while GEO covers generative AI more broadly, but the tactics used to improve either one are the same.
Does a high GEO Score guarantee more traffic? No. A high GEO Score means a brand is frequently and positively cited in AI answers, but many of those interactions end without a click, since answer engines increasingly resolve the query without sending the user anywhere.
How often do these metric definitions change? The underlying concepts are stable, but LLM providers update how their models retrieve and cite sources fairly often, so the practical weight of any single metric can shift over a few months.
Can a brand improve its Mention Rate without improving its website? Only partially. Structured, well-sourced content on the brand's own site is one input, but third-party coverage and independent mentions also influence how often an LLM brings a brand up.
What is the fastest metric to move? Extractability and citable block density tend to respond fastest, since they depend on how existing pages are structured rather than on building new authority or coverage.
Want to see these metrics calculated for your own brand? Get a free AEO Monitoring scan from AI, TELL ME! and see exactly where you stand.
