AEO and GEO Glossary
Answer Engine Optimization comes with its own vocabulary, and much of it overlaps or gets used loosely. This glossary defines the terms that come up most when you are working out how AI search finds, understands, and recommends a business. Each definition leads with the term itself, so you can lift the part you need and move on.
Anmol Talwar, founder of The AEO Loop
How to read these definitions
The terms below fall into a few loose groups. Some name the field itself (AEO, GEO, LLMO). Some name the systems doing the work (answer engines, generative engines, AI Overviews, AI Mode). Some describe how those systems find and assemble information (grounding, retrieval-augmented generation, citations, extractable passages). And some describe how you get understood and measured (entities, knowledge graphs, schema, share of voice, recommendation rate).
You do not need to memorize any of it. Skim for the term you hit in a proposal, an audit, or a sales call, read the two-sentence definition, and keep going. For the bigger picture of how these pieces fit together, start with our guide to what AEO is, and if you are weighing it against traditional search, AEO vs SEO is the companion read. When you want to see the concepts applied to real work, look at our method and the visibility scanner.
Every term, defined.
AEO (Answer Engine Optimization)
Answer Engine Optimization is the practice of making a business the answer an AI engine gives, rather than only a link it ranks. It covers the content, structure, and off-site signals that let systems like ChatGPT, Perplexity, Google AI Overviews, and Gemini retrieve and recommend you.
GEO (Generative Engine Optimization)
Generative Engine Optimization is the same discipline framed around generative search, shaping how a language model assembles an answer so your business appears inside it. In practice AEO and GEO describe overlapping work and are often used interchangeably.
LLMO (Large Language Model Optimization)
Large Language Model Optimization is another name for the same field, with the emphasis on the model itself as the target. The three acronyms, AEO, GEO, and LLMO, mostly reflect who coined them rather than different methods.
Answer Engine
An answer engine is a system that responds to a question with a direct, synthesized answer instead of a list of links. Perplexity and Google's AI Mode are examples, and the user often reads the answer without clicking through to any source.
Generative Engine
A generative engine is an AI system that writes a fresh response to each query using a language model, drawing on retrieved sources and its own training. Because the text is generated rather than looked up, the same question can return different answers on different runs.
Grounding
Grounding is the step where an engine ties its generated answer to real, retrieved sources rather than model memory alone. Gemini grounds answers in Google Search, and well-grounded answers are more likely to name and cite specific businesses.
Retrieval-Augmented Generation (RAG)
Retrieval-Augmented Generation is the architecture behind most answer engines: the system retrieves relevant documents, then feeds them to a language model that writes the answer. Your page has to survive the retrieval step before it can appear in the generated text.
Entity
An entity is a distinct, identifiable thing, such as a business, person, place, or product, that engines track as a single concept rather than a loose string of words. Being understood as a clear entity is what lets an engine connect your name, services, and location with confidence.
Knowledge Graph
A knowledge graph is a structured map of entities and the relationships between them that search and AI systems use to reason about the world. Google's Knowledge Graph is the best known, and consistent representation in it strengthens how AI systems describe you.
sameAs
sameAs is a schema property that links your entity to its other authoritative profiles, such as Wikipedia, LinkedIn, Crunchbase, or your social accounts. It tells engines that these all refer to the same business, which reduces ambiguity about who you are.
Structured Data / Schema.org
Structured data is machine-readable markup, built on the shared Schema.org vocabulary, that labels what each part of a page means. It is not required for generative answers, but it sharpens entity clarity and helps engines extract facts cleanly.
JSON-LD
JSON-LD is the recommended format for adding structured data: a block of JSON in the page that describes the entity without changing the visible layout. Google reads it directly, and it is the format most AEO work uses for schema.
AI Overview
An AI Overview is Google's generated summary that appears above the traditional blue links for many queries, answering the question on the results page itself. Appearing inside one is different from ranking first, and it often reduces clicks to any single site.
AI Mode
AI Mode is Google's fully conversational search experience, where the whole results page is a generated answer you can follow up on. It leans harder on retrieval and synthesis than AI Overviews, so being cited there depends on being retrievable and clearly authoritative.
Citation
A citation is the named source an answer engine links or attributes when it makes a claim. Earning citations is a core AEO goal because they carry your name into the answer and give readers a path back to you.
Extractable Passage
An extractable passage is a self-contained chunk of your content, often a direct definition or a short factual answer, that an engine can lift cleanly without the surrounding context. Writing in extractable passages makes your pages easier to quote and cite.
Hallucination
A hallucination is a confident but false statement an AI generates, such as inventing a service you do not offer or a detail about your business that is wrong. Clear, consistent, well-structured information across the web reduces the chance an engine gets you wrong.
Buying-intent query
A buying-intent query is a question from someone close to a decision, like "best [service] in [city]" or "who should I hire for X," rather than idle research. These are the answers worth being named in, because the reader is ready to act.
Share of Voice
Share of voice, in an AEO context, is how often your business appears across a set of AI answers compared with your competitors for the queries you care about. It measures presence in the answer, not position in a link list.
Recommendation rate
Recommendation rate is how often an engine actually names or recommends you when asked a relevant question. Because answers are non-deterministic, it is measured across many runs rather than from a single check.
Entity Home
An entity home is the single authoritative page, usually an About page or a dedicated entity page, that engines can treat as the canonical description of your business. A strong entity home, linked out through sameAs, anchors how systems understand you.
E-E-A-T
E-E-A-T stands for Experience, Expertise, Authoritativeness, and Trustworthiness, the qualities Google's guidelines use to judge source quality. It is not a direct score, but the signals behind it, such as real authorship, credentials, reviews, and consistent citations, influence whether AI systems treat you as a source worth naming.
Common questions.
Why do AEO, GEO, and LLMO all seem to mean the same thing?
Because three different groups coined names for the same shift: optimizing to be named inside AI answers instead of only ranking in links. AEO leads with "answer," GEO with "generative," and LLMO with the model itself. The work behind them overlaps heavily, so we treat the terms as near-synonyms and don't get precious about which one a client uses. If you want the full picture, our guide to what AEO is walks through it.
Do I need to understand all of these terms to do AEO?
No. A handful carry most of the weight: entity, grounding, citation, extractable passage, and recommendation rate. The rest are useful when you are reading an audit or comparing vendors. Start with the terms tied to how engines find and name you, and pick up the structural ones like schema, JSON-LD, and sameAs as they come up.
Which of these terms should I ask a potential AEO vendor about?
Ask how they measure recommendation rate and share of voice, since those tell you whether they track being named in answers rather than just counting traffic. Then ask how they build your entity, meaning your knowledge graph presence, sameAs links, and entity home, because that is what makes engines confident about who you are. Vague answers on either point are a warning sign.
How often does this vocabulary change?
The core concepts are stable, but the product names move quickly. Google renames and reshapes features like AI Overviews and AI Mode, and new engines appear regularly. We revise this glossary as the terms that matter in real client work shift, and keep the definitions tied to how engines actually behave rather than to any one company's branding.
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