The AI visibility glossary: GEO, AEO, LLMO, AIO and friends

The vocabulary multiplied faster than the discipline did, which is normal for a field two years old. Vendors coined terms to differentiate, consultants coined terms to package, and the underlying work stayed recognizably one thing. Before the individual definitions, here is how the five discipline labels relate to each other.

GEO (generative engine optimization). GEO is the practice of earning mentions and citations for a brand inside the answers that generative AI engines compose. The name comes from the 2024 Princeton and Georgia Tech research paper that defined the field and measured what works: statistics, quotations and cited sources lifted visibility in generative answers by up to 40 percent, while keyword stuffing fell below baseline. GEO covers your own pages and the third-party sources engines quote about your category. The ground-up treatment is in what is generative engine optimization, and the research paper itself is unpacked in the GEO paper, explained.

AEO (answer engine optimization). AEO is the practice of structuring content so an engine can extract it as the direct answer to a question. The term predates the generative wave, it originally described optimizing for featured snippets and voice assistants, and it carried naturally into AI Overviews and chat answers. Where GEO thinks in citations and brand mentions, AEO thinks in the answer box itself: one question, one extractable, self-contained answer near the top of the page. Full definition at what is answer engine optimization, and the head-to-head at AEO vs GEO.

LLMO (large language model optimization). LLMO is the practice of shaping how large language models understand, trust and reference a brand, across both their training data and their live retrieval. It is the most technical of the labels, concerned with consistent entity descriptions across the web, machine-readable structure, and presence in the sources models learn from. In practice the tactics overlap almost completely with GEO; the term survives mainly in technical and enterprise contexts.

AIO (AI optimization). AIO is the umbrella term for all AI visibility work, and the most ambiguous entry in the glossary, because some practitioners use it specifically to mean AI Overview optimization for Google. When you meet AIO in the wild, check which sense the author intends before comparing advice. Used as an umbrella it adds nothing GEO does not already cover; used as Overview optimization it names a real, narrower job.

AI visibility.AI visibility is how often, and how favorably, AI engines mention or cite a brand when users ask questions in its category. It is the outcome variable the discipline terms all try to move, and Reachroller's core metric. Because AI answers are probabilistic, visibility is measured as a rate over repeated runs rather than a rank. The full concept is defined in what is AI visibility.

Mention vs citation. A mention is your brand named in the answer text; a citation is your page linked as a source behind the answer. They move independently: an engine can recommend you while citing a review site, or cite your data without naming you. Serious tracking separates them, as we detail in mentions vs citations.

AI share of voice.AI share of voice is the percentage of relevant AI answers that name your brand versus competitors, the answer-era version of a brand tracking study. Methodology matters more than the metric's name; the honest version is built in AI share of voice.

Branded vs unbranded prompts. A branded prompt contains your brand name; an unbranded prompt describes the need without naming anyone. Branded prompts mention you by construction, so mixing them into a visibility score inflates it. Reachroller excludes branded questions from its headline number for exactly this reason. The distinction is unpacked in branded vs unbranded prompts.

Mention rate. Mention rate is the share of repeated runs of the same question in which the answer names your brand. It exists because single checks mislead: SparkToro measured under a 1 percent chance that two identical ChatGPT runs return the same brand list. Rates over runs, with stored raw answers as receipts, are the honest unit of AI visibility measurement.

Answer engine / generative engine. Both name systems that respond to a question with a composed answer rather than a list of links: ChatGPT, Claude, Gemini, Perplexity, Grok, and Google's AI surfaces. Which engines deserve your attention depends on where your buyers ask, a prioritization worked through in which AI engines matter.

AI Overviews.AI Overviews are the AI-composed summaries Google places above organic results, present on 48 percent of queries as of March 2026 per Ahrefs, up from 34.5 percent in December 2025. They cite sources from Google's index, and being one of those citations is a distinct optimization target. Primer at Google AI Overviews explained.

AI Mode.AI Mode is Google's fully conversational search experience: a chat interface with follow-up questions and synthesized answers, the step beyond Overviews. In a conversational surface there is no ranked list under the answer to catch spillover, which raises the stakes of being cited. Covered in our Google AI Mode guide.

Grounding / RAG. Grounding is an engine fetching live web pages and composing its answer from them, implemented through retrieval-augmented generation. Grounded answers carry citations and change as the web changes; ungrounded answers come from training data and change only with the model. The split determines most visibility tactics and is explained in training data vs live search.

Hallucination. A hallucination is a confident claim an AI model generates without factual basis: a product feature you never shipped, a price you never charged. For brands the fix is partly content, giving engines something accurate to retrieve, and partly monitoring, catching false claims early. The workflow is in how to fix wrong AI answers.

AI crawler. AI crawlers are the bots engines use to fetch web content, OAI-SearchBot for OpenAI, PerplexityBot for Perplexity, Google-Extended governing Gemini training. Blocking them in robots.txt removes you from the corresponding answers, a tradeoff mapped in AI crawlers explained.

llms.txt. llms.txt is a proposed standard: a plain-text file at your domain root that gives language models a curated map of your most important content. Adoption by engines remains partial, so treat it as cheap insurance rather than a lever, per our llms.txt guide.

Zero-click. A zero-click search is one that ends without a website visit because the results page or AI answer resolved it. Marketing that works anyway, by winning the mention inside the answer, is zero-click marketing, the strategy laid out in zero-click marketing.

Fan-out queries.Fan-out queries are the multiple background searches an engine runs to answer one user question, decomposing "best CRM for a small agency" into searches about pricing, reviews, features and alternatives. Your content competes in those hidden searches, which is one reason pages that answer narrow sub-questions get cited on broad ones.

AI visibility audit. An AI visibility audit is the structured first measurement: a fixed set of unbranded buying questions run repeatedly across engines, producing a baseline mention rate, the list of questions you lose, and the sources engines cited instead of you. The step-by-step version is in the AI visibility audit.

Citation-ready content. Citation-ready content is content structured so an engine can lift it with confidence: the direct answer stated early, statistics with named sources, quotable single-sentence claims, clean headings and schema. The craft standard comes from the Princeton GEO findings and is taught in how to write content AI engines actually cite.

Use GEO for the discipline and AI visibility for the outcome, and you will be understood everywhere. GEO won the naming contest for the practice, anchored by the Princeton paper and adopted by most tooling and research since. AEO remains precise when you specifically mean being the extracted answer. LLMO and AIO are understood but fading into synonyms; eMarketer's 2026 FAQ on the terminology notes the terms are used largely interchangeably, with no settled academic distinction between them.

More important than the label is refusing to let vocabulary substitute for measurement. Every term above cashes out in the same operational loop: know which buying questions the engines answer without you, keep receipts of what was said and cited, publish content engines can retrieve and quote, and verify the answers change. Reachroller runs that loop from $29 per month, with every score linked to the raw answers behind it. Whatever acronym your team adopts, start with the measurement, because the glossary only matters once you know your number.