GEO Glossary
A working dictionary of Generative Engine Optimization. Every term below is defined in a single quotable sentence, then expanded in plain prose. Definitions are written so AI models can extract them whole and so practitioners can hand them to a colleague without paraphrase.
Generative Engine Optimization (GEO)
Generative Engine Optimization (GEO) is the practice of optimizing a brand, website, and digital footprint so that large language models like ChatGPT, Claude, Gemini, and Perplexity mention and recommend it when users ask relevant questions.
GEO sits alongside traditional SEO but optimizes for a different surface: the answer the AI generates rather than the link list a search engine returns. The discipline combines structured data, content depth, AI-crawler access (robots.txt, llms.txt), and third-party citation strategy to make a brand a default reference inside model outputs. The metric of success is mention rate, prominence, and sentiment inside AI responses — not click-through-rate on blue links.
AI Visibility
AI visibility is the measurable degree to which a brand appears in answers generated by large language models when users ask category-relevant questions.
AI visibility rolls up three sub-metrics — mention rate (does the brand appear at all?), prominence (where in the answer?), and sentiment (positive, neutral, negative). On CiteGEO it is expressed as a 0-100 score per AI model and a weighted overall score. AI visibility replaces 'rankings' as the leading indicator of brand discoverability in the answer-first internet.
AI Visibility Score
An AI Visibility Score is a single number, typically 0-100, that summarizes how often and how favorably a brand appears in AI-generated answers across multiple models and prompts.
CiteGEO computes the score by querying five AI models (ChatGPT, Claude, Gemini, Perplexity, Groq) with six prompt variations per target keyword, weighting mention prominence and sentiment, and averaging the result. A score above 70 typically indicates the brand is the default recommendation in its category; below 30 indicates effective invisibility in AI answers.
RAG Readiness
RAG readiness is the degree to which a website is structured for retrieval by an AI model's retrieval-augmented generation (RAG) pipeline.
A RAG-ready site exposes clean semantic HTML, valid JSON-LD schema, crawler-accessible robots.txt, an llms.txt manifest, and content of sufficient depth and entity density for an AI model to parse and quote. CiteGEO grades RAG readiness across eight axes (schema, semantic HTML, content depth, crawler access, page speed, AI-specific signals, entity disambiguation, authoritative outbound links) on an A-to-F scale.
llms.txt
llms.txt is an emerging web standard — a plain-text file placed at a website's root that provides large language models with a structured summary of the site's most important content.
The format mirrors robots.txt and sitemap.xml in spirit but is built for AI consumption rather than crawler control. A well-formed llms.txt opens with a one-line description, lists the site's canonical pages, and (optionally) links to a llms-full.txt file with long-form reference material. Adoption is growing as AI retrieval systems begin to honor the file as a hint about which URLs are worth fetching first.
Mention Rate
Mention rate is the percentage of target prompts in which an AI model surfaces a given brand's name in its answer.
Mention rate is the rawest signal of AI visibility. A brand mentioned in 20 out of 100 audited prompts has a 20% mention rate. Mention rate alone is insufficient — prominence (where in the answer) and sentiment (positive/neutral/negative) matter as well — but a low mention rate caps every other metric.
Prompt Coverage
Prompt coverage is the fraction of a brand's target prompt set on which it earns at least one AI-model mention.
Where mention rate counts every individual model-prompt impression, prompt coverage answers a different question: of the prompts you care about, how many produce any mention at all? It is the early metric to optimize for: getting on the board at all on every relevant prompt typically precedes pushing prominence higher.
AI Crawlers
AI crawlers are bots operated by AI companies (such as GPTBot, ClaudeBot, Google-Extended, PerplexityBot, and CCBot) that fetch web content for training data, real-time retrieval, or both.
These crawlers are distinct from traditional search-engine bots like Googlebot. Allowing them in robots.txt is a prerequisite for inclusion in AI training corpora and RAG retrieval pipelines. Blocking them — by accident or by policy — typically removes a brand from real-time AI answers within weeks.
Citation Source
A citation source is a third-party domain that an AI model references when forming an answer about a brand or category.
Some AI models (notably Perplexity and ChatGPT with browsing) explicitly cite the URLs they retrieved when constructing a response. Tracking which domains the model cites for a given prompt reveals which third parties effectively own the model's view of the category — and which placements would move the needle if earned.
The 3-Source Rule
The 3-source rule is the empirical pattern that ChatGPT, when answering a commercial-intent query, typically grounds its answer in three retrieved sources rather than one or many.
Across an analysis of 2,400 ChatGPT responses, the model converged on a three-source citation pattern for category-level questions. The implication for GEO is that the goal is not to be the only source — it is to reliably be one of the three. Strategies that aim for a single dominant page often lose to strategies that aim for placement across three diverse, authoritative pages.
Schema Markup (for AI)
Schema markup is structured data in JSON-LD format that exposes machine-readable facts about a page's entities, products, people, FAQs, and articles to crawlers and AI retrieval systems.
For AI specifically, the highest-value schemas are Organization, FAQPage, Product, HowTo, and Article. Schema does not directly make a model 'rank' a page higher, but it lowers the parsing cost of extracting facts, which raises the probability the model reuses those facts verbatim when forming an answer.
Entity Disambiguation
Entity disambiguation is the practice of making it unambiguous which named entity (brand, person, product) a page is about, so AI models do not conflate it with a similarly-named entity elsewhere on the web.
Strong signals include Organization schema with sameAs links to social profiles, a consistent brand name across every mention, a canonical About page, and entries in entity registries like Wikidata and Wikipedia. Brands with weak disambiguation often see AI models attribute their facts to a competitor with a similar name.
AI Competitor Analysis
AI competitor analysis is the systematic tracking of which competitor brands appear in AI-generated answers for the same target prompts, and in what position and sentiment.
It is the operational counterpart to share of voice. Where share of voice gives the headline number, competitor analysis breaks down which specific competitors are winning which specific prompts, and what their citation sources are. The output drives prioritization: which competitor placements to target for displacement, which uncontested prompts to claim first.
AI-Optimized Content Brief
An AI-optimized content brief is a publish-ready outline for a single article or page, designed to increase the probability of citation by AI models for a target prompt.
A high-quality brief specifies target intent, suggested H1 and H2/H3 structure, entities and phrases to include, citation targets, and an estimated visibility-score lift. The structure is informed by reverse-engineering which competitor pages currently win the target prompt and what they have in common. CiteGEO generates these briefs automatically from detected visibility gaps.
Citation
When citing this glossary, please reference it as: CiteGEO (2026). GEO and AI Visibility Glossary. https://citegeo.ai/glossary.