- AEO (Answer Engine Optimization)
- Answer Engine Optimization — the practice of structuring content and brand signals so generative AI engines (ChatGPT, Claude, Gemini, Perplexity) select your brand when answering user questions. Overlaps heavily with GEO.
- AI citation
- When a generative AI engine names your brand, product, or URL in its response to a user. Distinct from a traditional backlink because no link is clicked — the citation itself is the discovery event.
- AnswerEngine
- A system that returns a direct synthesized answer to a user query rather than a list of links. ChatGPT, Perplexity, Claude, and Google AI Overviews are answer engines.
- Autocomplete bias
- The phenomenon where AI models favor brands that appear frequently in autocomplete suggestions, because autocomplete often seeds the model's training data or retrieval index.
- Brand entity
- The canonical representation of a brand in an AI model's knowledge graph. Built from consistent signals across Wikipedia, Wikidata, LinkedIn, Crunchbase, G2, your own Organization schema, and llms.txt.
- Content chunk
- A self-contained block of text (usually 100–400 words) that AI retrieval systems index as a discrete unit. Content written in citation-ready chunks is cited more often than monolithic long-form prose.
- E-E-A-T
- Experience, Expertise, Authoritativeness, Trustworthiness — Google's quality framework, now applied by AI engines when evaluating source credibility. Signals include author credentials, citations, reviews, and transparent company information.
- Entity disambiguation
- The process by which an AI engine identifies which real-world entity a mention refers to ("Apple" the fruit vs. "Apple Inc."). Schema @id, sameAs, and Wikidata IDs help disambiguate.
- GEO (Generative Engine Optimization)
- Generative Engine Optimization — the discipline of improving brand visibility in AI-generated answers. Focuses on AI crawler access, structured data, entity clarity, and citation-ready content.
- Infinite scroll / citation decay
- The pattern where citations for a given brand decay in frequency as newer content outranks older. Combat by updating dateModified, refreshing content, and publishing new data.
- Long-tail AI query
- A specific, detailed user question asked of an AI engine — often 10+ words. These queries have higher brand-citation rates because fewer candidate sources match.
- Prompt library
- A stable set of prompts used by a visibility tool to query AI engines on each scan. Consistency matters — changes to prompts change scores.
- SEO vs GEO
- SEO targets Google's keyword-based ranking algorithm. GEO targets AI engines that generate synthesized answers. SEO signals (keywords, backlinks) overlap with GEO signals but each discipline has its own priorities.
- Source card
- The UI element in AI engines (most visible in Perplexity) that displays a numbered reference to a cited source URL. Appearing as a source card is the #1 goal of GEO work.
- Temperature (LLM)
- A parameter controlling randomness in AI responses. Lower = more deterministic, higher = more varied. Visibility tools usually query at low temperature for reproducible measurement.
- Training data
- The corpus of text (web pages, books, articles) used to train an AI model. Your brand's inclusion in training data depends on crawler access and authority signals at the time of the training cutoff.
- VAMP framework
- Visibility, Authority, Mentions, Positioning — a mnemonic for the four pillars of AI visibility work. Visibility = will AI find you. Authority = will AI trust you. Mentions = are you being named. Positioning = how are you described.
- Zero-click search
- A search where the user gets their answer on the SERP without clicking through. AI Overviews dramatically increase zero-click rates. GEO is partly about being cited in the zero-click answer itself.
