Glossary
The language of AI search.
Plain-English definitions of the terms behind AI visibility, GEO and AEO, written so an AI can quote them.
- AI visibility
- AI visibility is how often, how prominently, and how positively AI answer engines like ChatGPT, Perplexity and Google AI Overviews mention your brand when people ask questions. It is the AI-era equivalent of a search ranking: if the answer names competitors and not you, you are invisible where the buying decision now happens.
- Generative Engine Optimization (GEO)
- Generative Engine Optimization (GEO) is the practice of getting generative AI engines to recommend and cite your brand in their answers. Where SEO optimizes for ranked links, GEO optimizes for being named in the answer itself, using signals like third-party citations, structured data, llms.txt and answer-first content.
- Answer Engine Optimization (AEO)
- Answer Engine Optimization (AEO) is optimizing your content to be the answer an AI or search engine gives directly, rather than one of ten links. It overlaps with GEO and focuses on clear, answer-first writing, FAQ and Organization schema, and being the source the engine trusts enough to quote.
- AI Overview
- An AI Overview is the AI-generated answer box Google shows at the top of many search results, summarizing an answer and citing a few sources. Appearing (and being cited) in AI Overviews is a core GEO goal because the box often satisfies the query before a user scrolls to the classic links.
- Answer engine
- An answer engine is any AI system that responds to a question with a synthesized answer instead of a list of links. Examples include ChatGPT, Claude, Perplexity, Google Gemini, Google AI Overviews and Microsoft Copilot. These are the surfaces GEO and AEO target.
- Citation
- A citation is a source an AI answer links to or draws from when it makes a claim. The domains an engine cites in your category reveal which sources it trusts; earning citations from those sources, and being one of them, is how you move an AI answer in your favour.
- llms.txt
- llms.txt is a plain-text file published at yoursite.com/llms.txt that gives AI models a clean, structured map of your site: what you do and where the authoritative pages live. It helps answer engines understand and cite you accurately, the way robots.txt and sitemaps help traditional crawlers.
- Query fan-out
- Query fan-out is when an AI answer engine expands a single question into many parallel sub-queries, searches each, and synthesizes one answer. Understanding the fan-out shows the real sub-questions buyers' queries trigger, so you can create content that wins those sub-queries.
- Buyer-intent prompt
- A buyer-intent prompt is a real question a customer would ask an AI while choosing what to buy, such as "best CRM for a small sales team" or "alternatives to X". Measuring visibility on these prompts, rather than generic keywords, is what makes AI-visibility tracking commercially meaningful.
- Sentiment
- Sentiment in AI answers is whether an engine speaks about your brand positively, neutrally or negatively. Being named is not enough; if the engine frames you as risky or dated while praising a rival, the mention works against you. GEO tracks the tone, not just the presence.
- Visibility Score
- A Visibility Score is a single 0–100 metric that rolls up mention rate, position and citations across engines and buyer questions into one number you can track over time. It turns scattered AI answers into a defensible measurement you can improve and prove.
See these on your own brand.
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