AI search glossary

AI Overview (formerly Google SGE) is Google's AI-generated answer summary that appears at the top of search results. Powered by Google's Gemini model, it synthesizes information from top-ranking pages into a direct answer with source citations — so users often don't need to click through.

AI search is a search technology in which artificial intelligence models — typically large language models (LLMs) — generate natural-language answers to questions rather than presenting a list of links. Examples include ChatGPT Search, Google AI Overviews, Perplexity, and Microsoft Copilot.

AI search monitoring is the systematic tracking of a brand's visibility, citations, and mentions across AI search engines and chatbots — including ChatGPT, Gemini, Perplexity, and Google AI Overviews — to measure performance over time and identify optimization opportunities.

AI visibility is a brand's or organization's ability to be mentioned, recommended, or cited by AI search systems such as ChatGPT, Gemini, Perplexity, and Google AI Overviews when users ask relevant questions.

Answer Engine Optimization (AEO) is the discipline of optimizing content and digital presence to be selected as the answer — or cited as a source — by AI-driven answer engines such as ChatGPT, Gemini, Perplexity, and Google AI Overviews. It stands in contrast to traditional SEO, which optimizes for click-through ranking in search result lists.

Brand visibility in AI is the extent to which, and the manner in which, a specific brand is mentioned, described, or recommended by AI search models and chatbots. It is a composite KPI that includes citation rate, sentiment, and context quality.

CAVIS (Conversational AI Visibility Intelligence System) is a framework for systematic simulation and analysis of AI visibility, developed by CitationLab AS. CAVIS structures AI search simulations around real user situations rather than simple keywords, and produces quantifiable visibility metrics across AI models.

ChatGPT SEO is the set of strategies and techniques used to increase the likelihood that ChatGPT will mention or recommend a particular brand, website, or source in its answers — whether through training-data optimization, live web retrieval (ChatGPT Search), or by influencing content indexing.

The systematic work of increasing the number and quality of brand mentions in AI-generated answers.

Citation rate is the share of relevant AI search queries in which a specific brand, website, or source is mentioned or cited in the AI model's answer. It is a core KPI for AI visibility and is calculated as: (number of answers with a mention) / (total prompts run) × 100.

E-E-A-T (Experience, Expertise, Authoritativeness, Trustworthiness) is the quality and credibility framework used by Google and AI models to evaluate content. Strong E-E-A-T signals increase the likelihood that content is included in AI answers and ranks highly in search.

Entity SEO is the optimization of digital presence around entities — named, distinct concepts such as people, places, organizations, and ideas — so that search engines and AI models can identify them and connect them to relevant knowledge in their knowledge graphs.

Gemini SEO is the optimization of content and presence to be cited by Google's Gemini model — including in Google AI Overviews, Google Search, and Gemini Assistant. Because Gemini is tightly integrated with Google Search, Gemini SEO overlaps heavily with traditional SEO.

Generative Engine Optimization (GEO) is a discipline within digital marketing that optimizes content and digital presence to be included in answers generated by generative AI models. GEO and AEO are often used interchangeably, but GEO places particular emphasis on content-production strategy rather than technical optimization.

A knowledge graph is a structured database that represents entities (people, places, concepts, organizations) and the relationships between them. Google's Knowledge Graph and the internal representations of AI models are knowledge graphs that shape which information is presented in search and AI answers.

An LLM citation occurs when a large language model (LLM) explicitly mentions, references, or recommends a specific source, brand, or website in an answer generated for a user — whether as an attributed source, a recommendation, or part of a factual claim.

LLM training data is the massive text corpora that large language models learn from during training — including web pages, books, academic papers, and databases. Being represented in these sources is foundational for a brand to be mentioned by LLMs without live search.

A standardized text file in the root directory that gives AI crawlers contextual information about a website's content and structure.

Perplexity SEO is the optimization of website content and authority specifically to be cited by Perplexity AI — a search engine that combines live web retrieval with LLM synthesis to deliver sourced answers. Perplexity cites its sources explicitly and therefore drives direct link traffic.

Prompt visibility is the extent to which a brand, product, or source appears in AI model answers to specific prompts (questions or instructions) relevant to the business's offering. It is measured by systematically running representative prompts and recording occurrences.

Retrieval-Augmented Generation (RAG) is an AI architecture in which a model retrieves relevant documents from an external knowledge base and uses them as contextual grounding to generate an answer — rather than relying solely on training data.

Search grounding is the technique in which an AI model supplements its internal training knowledge with live web retrieval to deliver more current and accurate answers. The model searches the web, fetches relevant texts, and uses them as the ground truth for the answer.

Structured data for AI is schema.org markup and other machine-readable metadata formats that help AI models and search engines understand the context, type, and relationships of content — and that increase the likelihood of content being used as a source in AI-generated answers.

The degree of subject-matter depth and content breadth a website has within a specific topic area, as assessed by search engines and AI models.