The plain-English glossary for AI visibility.

Generative Engine Optimization (GEO) is the practice of shaping web content, structure and authority signals so that generative AI engines such as ChatGPT, Perplexity and Google AI Overviews recommend or cite a brand in their synthesized answers.

Answer Engine Optimization (AEO) is the practice of structuring content so that answer engines, including AI chatbots and search features that return a direct response, pick a brand or its content as the answer rather than just one of many links.

AI Optimization (AIO) is a broad umbrella term for any practice that improves how AI systems perceive, retrieve and recommend a brand, covering both content surfaces (chatbots, AI search) and off-surface signals (training data, knowledge graphs, mentions).

Search Engine Optimization (SEO) is the practice of improving a websites visibility in traditional search engines such as Google and Bing through keywords, technical health, content quality and backlinks, so that pages rank well for the queries users type into a search box.

Large Language Model Optimization (LLMO) is the practice of optimizing a brands content and signals so that large language model assistants such as ChatGPT, Perplexity, Gemini and Copilot reliably cite, mention or recommend the brand inside their generated answers.

Hybrid Engine Optimization (HEO) is the practice of treating classical search visibility and AI answer visibility as a single combined system to measure and optimize together, rather than as two separate channels, because optimizing one in isolation (or trading one for the other) usually produces a net loss in total discovery.

An answer engine is a search or chat product that returns a direct synthesized answer to a question instead of (or in addition to) a list of links, with ChatGPT, Perplexity, Google AI Overviews, Gemini and Copilot being the most widely used examples.

A large language model (LLM) is a machine learning model trained on huge amounts of text to predict the next token in a sequence, which lets it generate fluent natural-language responses and power products such as ChatGPT, Perplexity, Gemini and Copilot.

AI Overviews are Googles AI-generated answer summaries that appear at the top of a search results page, combining a synthesized paragraph or list with linked source citations, and they are the production successor to the earlier Search Generative Experience (SGE).

An AI crawler is an automated user agent operated by an AI company that fetches public web pages to use either for training large language models or for real-time grounding inside AI answers, with named examples including GPTBot, ClaudeBot, PerplexityBot, Google-Extended and CCBot.

Semantic search is a retrieval technique that matches a query to documents by the meaning of the text rather than by exact keywords, usually by converting both the query and the documents into vector embeddings and finding the closest matches.

A zero-click search is one where the user gets the answer directly inside the results page or AI overview without clicking through to any underlying site, and the share of such searches has risen sharply with the rollout of AI Overviews and chatbots.

Retrieval-augmented generation (RAG) is an AI architecture that first retrieves relevant documents from an external source and then feeds them to a language model so the model can ground its answer in those documents rather than relying only on what it memorized during training.

Grounding is the practice of constraining an AI models answer to specific retrieved sources so that the response is supported by evidence from those sources rather than generated freely from the models internal knowledge.

A vector embedding is a numerical representation of a piece of text (or other data) as a list of numbers in a high-dimensional space, designed so that texts with similar meaning end up close together and can be compared by a fast distance calculation.

A knowledge graph is a structured database that represents real-world entities (people, companies, products, places) as nodes and the relationships between them as edges, used by search engines and AI systems to reason about who is who and what is connected to what.

A hallucination is an AI-generated statement that is presented as factual but is actually invented, distorted or otherwise unsupported by reliable sources, and it is one of the central risks of using language models in answers about brands.

Brand visibility in AI refers to how often and how prominently a brand appears in answers produced by AI engines such as ChatGPT, Perplexity, Gemini and Google AI Overviews, measured across the queries that matter to the brand.

Share of voice in AI is the percentage of relevant AI answers in which a brand is named or cited, measured against a fixed set of prompts and against a defined competitive set, so that brand performance can be compared head to head over time.

An entity is a distinct, identifiable thing in the world (a company, product, person, place or concept), and entity authority is the strength of the signals that connect a brand to its entity record and to the topics it should be associated with in search and AI systems.

Prompt discovery is the practice of identifying the prompts and questions that real users actually type into AI engines about a brands category, so that visibility can be measured and improved against the queries that matter rather than against guessed-at keywords.

Query fan-out is the process by which an AI search system rewrites a single user query into many related sub-queries, retrieves results for each of them and then synthesizes a combined answer, which means a brands visibility depends on being relevant to the expanded query set, not just the original prompt.

A citation in an AI answer is an explicit reference to a source, usually a clickable link, that the engine attributes the information to, and it is the strongest form of visibility because it both names the source and sends traffic back to it.

A mention is when an AI answer names a brand without linking to it, while a citation is when the answer explicitly attributes information to a source via a link or footnote, which means mentions build awareness but citations build traffic and stronger authority signals.

Citation rate is the percentage of AI answers in a measured prompt set that include a citation linking back to a brands content, and it is the cleanest single metric for tracking whether AI engines treat the brand as an authoritative source.

Mention rate is the percentage of AI answers in a measured prompt set that name the brand at all, with or without a citation, and it is the broadest single measure of whether the brand exists in AI-generated answers about its category.

Sentiment in AI answers is the tone with which a brand is described inside generated text (positive, neutral or negative), measured to detect whether AI engines are recommending the brand favorably, treating it neutrally or actively warning users away from it.

llms.txt is a proposed plain-text file placed at the root of a website that gives large language models a concise, curated map of the sites most important pages and content sections, so AI systems can find the right pages without having to crawl the entire site.

robots.txt for AI crawlers is the use of the standard /robots.txt file to allow or block specific AI user agents such as GPTBot, ClaudeBot, PerplexityBot and Google-Extended, controlling which AI systems can access the sites content for training or real-time grounding.

Structured data is machine-readable markup, most commonly in JSON-LD format using the schema.org vocabulary, that labels the meaning of content on a page (Organization, Product, FAQPage, Article, Breadcrumb) so search engines and AI systems can parse it without having to guess from raw HTML.