Glossary
The terms you’ll hear when people talk about getting found by ChatGPT, Claude, Perplexity and Gemini, each defined in a sentence and explained in a paragraph.
Making a business easy for AI assistants to understand, trust and recommend.
AI SEO is the practice of improving how AI assistants such as ChatGPT, Claude, Perplexity and Gemini find, describe and recommend a business. It combines clear entity information, answer-first content, structured data, crawler access and a strong presence on the other sites those assistants read.
Improving how often and how accurately generative AI tools mention a brand.
GEO focuses on the answers produced by generative AI tools rather than on lists of search results. The goal is to be mentioned, described accurately and cited as a source when people ask questions related to what you do.
Structuring content so it can be lifted as a direct answer.
AEO is about making content easy for search engines and assistants to extract as a direct answer: a clear question as the heading, a concise answer in the first sentence or two, and supporting detail after it. It overlaps heavily with GEO and featured-snippet optimisation.
Visibility inside large-language-model answers.
LLM SEO is another name for improving how large language models, the technology behind AI assistants, describe and recommend a business. In practice it covers the same work as AI SEO and GEO.
The AI model behind assistants like ChatGPT, Claude and Gemini.
A large language model is an AI system trained on large amounts of text to understand and generate language. Assistants built on LLMs answer questions from what they learned in training and, increasingly, from pages they retrieve from the web at the time of the question.
When an AI assistant looks things up before it answers.
Retrieval-augmented generation is a technique where an AI system searches for relevant documents, reads them and uses them to write its answer, often citing them as sources. It is why being findable, fetchable and clearly written matters for AI visibility.
The text an AI model learned from before release.
Training data is the large body of text a model learned from before it was released. What a model “knows” without searching comes from its training data, which is why being described consistently across the web over time also matters.
Google’s AI-generated summaries at the top of some search results.
AI Overviews are summaries written by Google’s AI that appear above the traditional results for some searches. They cite a small number of sources, so being one of those sources has become an important part of search visibility.
A source an AI assistant links to or names in its answer.
A citation is a page or source that an AI assistant references in its answer. Being cited sends visitors and signals trust. Pages with clear, specific, well-structured answers are easier to cite.
How often and how well AI assistants mention a brand.
AI visibility describes how often a brand is mentioned in AI answers for relevant questions, how accurately it is described, and whether its pages are cited. It is usually measured by asking a fixed set of buyer questions across several assistants over time.
The percentage of relevant AI answers that mention a brand.
Share of answers is a way to measure AI visibility: out of a fixed set of relevant questions, the percentage of answers that mention your brand, compared with competitors. It is the AI-search equivalent of share of voice.
Re-running the same buyer questions in AI assistants over time.
Prompt tracking means asking AI assistants the same set of questions at regular intervals and recording who is mentioned, how, and which sources are cited. It turns AI visibility from guesswork into a trend you can act on.
A distinct, identifiable thing: a business, person, product or place.
In search and AI, an entity is something that can be uniquely identified, such as your company, your founder or your product. Search engines and AI systems build knowledge about entities from consistent facts across many sources, so clear and consistent details help them understand who you are.
A network of facts about entities and how they relate.
A knowledge graph stores facts about entities and the relationships between them, for example that a company offers a service and was founded by a person. Search engines use knowledge graphs to understand and describe businesses.
Code that describes a page’s content in a standard format.
Structured data is machine-readable information, usually written in JSON-LD using the schema.org vocabulary, that tells search engines and other systems what a page is about: an organisation, a service, an article, an FAQ. It should always match what the page visibly says.
Structured data that marks up questions and their answers.
FAQ schema (FAQPage in schema.org) marks up a list of questions and answers on a page. Even where search engines no longer show it as a rich result, it makes question-and-answer content explicit and easy to parse.
A plain-text summary of a website written for AI tools.
llms.txt is a proposed convention: a markdown file at the root of a website that summarises what the site is about and links to its most useful pages, so AI tools and agents can understand it quickly. It is not a ranking factor on its own, but it is cheap to add and helps agents that use it.
A file that tells crawlers which parts of a site they may visit.
robots.txt sits at the root of a website and sets rules for automated crawlers. Blocking AI crawlers there, often by accident or through a firewall default, can stop AI assistants from reading and citing your pages.
The bots AI companies use to read web pages.
AI crawlers are automated agents such as GPTBot and OAI-SearchBot (OpenAI), ClaudeBot (Anthropic) and PerplexityBot (Perplexity) that fetch web pages for training or for answering questions in real time. Allowing the ones you want is a basic step in AI visibility.
How easily automated systems can reach and read your pages.
Crawlability describes whether search engines and AI crawlers can access your pages and read their content. Broken links, blocked resources, heavy client-side rendering and login walls all reduce it.
Experience, Expertise, Authoritativeness and Trustworthiness.
E-E-A-T is a framework from Google’s search quality guidelines for judging content quality: the experience and expertise behind it, the authority of the source and how trustworthy it is. Named authors, evidence, clear sourcing and a strong reputation elsewhere all support it.
Giving the direct answer before the detail.
Answer-first writing puts a clear, complete answer to the heading’s question in the first sentence or two, followed by supporting detail. It serves people who skim and makes the passage easy for search engines and AI assistants to lift.
A search where the answer appears without anyone clicking a result.
A zero-click search is one where the searcher gets what they need directly on the results page or in an AI answer, without visiting a website. It makes being named and cited in those answers more valuable.
What someone is actually trying to achieve with a search.
Search intent is the goal behind a query: to learn something, compare options, find a specific site, or buy. Matching each page to the intent behind the searches it targets is at the heart of both SEO and AI SEO.
The infrastructure work that lets search engines crawl, render and index a site.
Technical SEO covers site structure, crawlability, indexing, page speed, Core Web Vitals, canonical URLs, sitemaps and structured data: everything that helps search engines and AI crawlers access and understand a website.
The preferred address for a page that could appear at several URLs.
A canonical URL tells search engines which version of a page is the main one when the same content is reachable at more than one address. It consolidates signals and avoids duplicate-content confusion.
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