AI visibility glossary
The core vocabulary of AI visibility, Answer Engine Optimization (AEO), and Generative Engine Optimization (GEO). Each term links to its own anchor so you can cite it directly.
AI visibility
The practice of structuring a brand's expertise so large language models can find, interpret, and cite it inside generated answers.
AI visibility is the umbrella discipline covering both Answer Engine Optimization (AEO) and Generative Engine Optimization (GEO). A brand is AI-visible when answer engines like ChatGPT, Perplexity, Claude, and Google AI Overviews can (a) resolve queries to that brand as an entity, (b) retrieve pages from its site with confidence, and (c) reproduce claims from those pages inside generated answers, ideally with attribution. AI visibility depends on entity clarity, retrieval-readiness, topical authority, structured evidence, and decision-stage content — the five domains of the CLEAR framework.
Answer Engine Optimization (AEO)
Optimizing content and structure so answer engines surface a specific source inside a generated response.
Answer Engine Optimization (AEO) is the page-level practice of engineering content so it can be extracted verbatim by answer engines — ChatGPT, Perplexity, Claude, and Google AI Overviews. AEO focuses on retrieval-readiness signals: a single H1, an answer-first first paragraph in 40–80 words, an on-page FAQ block, JSON-LD schema (Organization, Service, FAQPage, Article), canonical URLs, and complete Open Graph tags. AEO is the tactical layer of AI visibility; GEO is the broader strategic layer.
Generative Engine Optimization (GEO)
The broader discipline of influencing generative search systems — retrieval-augmented generation pipelines, model training data eligibility, and citation graphs.
Generative Engine Optimization (GEO) is the strategic discipline of influencing which sources a generative AI system trusts, retrieves, and cites when it composes answers. GEO covers three surfaces: (1) retrieval-augmented generation (RAG) — being present in the vector index the model queries at runtime; (2) training data eligibility — being present in the crawls used to train or fine-tune models; and (3) citation graphs — accruing the internal and external links models use as authority proxies. GEO subsumes AEO but adds authority architecture, entity linking, and long-horizon reputation signals.
Retrieval-Augmented Generation (RAG)
The architecture answer engines use to ground a generated response in retrieved documents rather than model memory alone.
Retrieval-Augmented Generation (RAG) is the standard architecture behind answer engines like Perplexity, Google AI Overviews, and ChatGPT's browsing modes. When a user asks a question, the system (1) retrieves candidate passages from a live index or the open web, (2) ranks them by relevance and trust, and (3) hands the top results to a large language model that composes the answer citing those sources. RAG makes AI visibility largely a retrieval problem: a page that isn't indexed, isn't retrievable, or doesn't chunk cleanly cannot be cited, no matter how good the underlying content is.
Retrieval readiness
A page's mechanical fitness to be pulled into a generated answer.
Retrieval readiness is the sum of on-page signals that determine whether an answer engine will pull a page into a generated response. The canonical checklist: a title tag under 60 characters, a meta description under 160 characters, a single H1, an answer-first first paragraph (40–80 words, entity named up front), a visible FAQ block on high-intent pages, JSON-LD schema (at minimum Organization + WebSite; ideally Service, FAQPage, Article, and BreadcrumbList), a self-referencing canonical URL, complete Open Graph and Twitter Card tags, and clean HTML the crawler can parse without JavaScript execution. ClearVisibility scores retrieval readiness per URL as part of the ClearVisibility Score™.
Citation-worthiness
The combination of entity clarity, structured evidence, and authoritative linking that leads a model to name a source unprompted in category-level answers.
Citation-worthiness is the qualitative property that determines whether a model will cite a brand by name when nothing in the prompt forces it to. Retrieval readiness gets a page into the candidate set; citation-worthiness decides whether the model actually attributes a claim to that source in the final answer. Signals that raise citation-worthiness: an unambiguous entity (name, category, sameAs links), a consistent one-sentence description used across the site, structured proof (case studies, benchmarks, named outcomes wrapped in Article or Review schema), and dense internal linking between pillar and supporting content on the topics the brand wants to own.
Entity clarity
The degree to which answer engines can resolve a brand to a single, well-defined entity with clean attributes and authoritative links.
Entity clarity is the foundation of AI visibility. Answer engines resolve a query to entities — brands, products, people, categories — before they generate an answer. If your organization isn't cleanly defined, or its category positioning is ambiguous, the model either substitutes a competitor or defaults to a generic descriptor. Fix entity clarity with a canonical one-sentence description repeated across pages, Organization + Service JSON-LD with knowsAbout and areaServed, a Person schema for named authors, and sameAs links pointing to LinkedIn, X, Crunchbase, and Wikidata where present.
llms.txt
A plain-text file served at the site root that gives answer engines a curated brief — organization summary, key resources, and definitions the brand wants cited verbatim.
llms.txt is a proposed convention (analogous to robots.txt) for giving large language models a curated brief about a site. Hosted at /llms.txt, the file lists the organization's summary, primary resources with URLs, key definitions written for verbatim citation, and pointers to canonical documents. Unlike robots.txt, which governs crawl permissions, llms.txt governs interpretation — it tells the model what the brand wants to be understood as. Complements the JSON-LD bundle on the site and shortens the model's path to a citation.
See these terms applied to your site
The ClearVisibility Score™ benchmarks your entity clarity, retrieval readiness, and topical authority across 60 category queries.