The language of AI visibility.

#GEOGenerative Engine Optimization

Generative Engine Optimization (GEO) is the discipline of preparing content and entities so that generative AI systems like ChatGPT, Perplexity, Gemini and Google AI Overviews cite them as a source. GEO shifts the goal from "clicks on position 1" to "cited in the AI answer".

Also called: AI-SEO, LLMO, AEO

#AEOAnswer Engine Optimization

Answer Engine Optimization (AEO) is the optimisation of content for answer engines that deliver direct answers instead of a list of links. AEO overlaps strongly with GEO; the term emphasises concise, directly answerable questions (answer-first content).

#LLMOLarge Language Model Optimization

Large Language Model Optimization (LLMO) refers to targeted optimisation of content for large language models. In practice it is used largely synonymously with GEO and emphasises the technical readability and consistency an LLM needs to reproduce a source correctly.

Also called: GEO

#AI-SEOAI search engine optimisation

AI-SEO is an umbrella term for search engine optimisation in the age of AI. It is often used synonymously with GEO but is broader: it covers both classic SEO for AI-assisted search and optimisation for purely generative answer systems.

Also called: GEO, LLMO

#llms.txtLLM hint file

llms.txt is a standardised Markdown file in the root of a domain that provides AI systems with a curated, bundled overview of a website's most important content. It works like a signpost for LLMs, comparable to what robots.txt does for classic crawlers.

#CitationSource reference

A citation is the named mention and usually the link to a source within an AI-generated answer. Citations are the central currency of GEO: whoever is cited is visible to the user, even if they never click through to the website.

#EntityEntität

An entity is a uniquely identifiable concept - a person, organisation, product or place. Search engines and LLMs link entities in a knowledge graph. A clearly defined, consistently described entity is recognised and assigned more reliably by AI systems.

#Canonical Entity Description

A canonical entity description is a fixed, identically used description of an entity such as a company. Because the same core description appears consistently on the website, in directories and in structured data, AI systems assign the entity unambiguously and reproduce it correctly.

#Knowledge Graph

A knowledge graph is a structured knowledge base that maps entities and their relationships to one another. Prominent examples are the Google Knowledge Graph and Wikidata. An entry increases the likelihood that an entity is referenced correctly in AI answers.

#Answer Engine

An answer engine is a system that delivers a direct, summarised answer to a query instead of a list of links. Examples are Perplexity, ChatGPT with web search and Google AI Overviews. Answer engines are the primary target of GEO and AEO.

#Google AI OverviewsAI summaries in Google Search

Google AI Overviews are AI-generated summaries that Google shows above the classic search results. They summarise several sources and link them. Visibility in AI Overviews is often the first measurable GEO success for many companies.

Also called: SGE

#RAGRetrieval-Augmented Generation

Retrieval-Augmented Generation (RAG) is a technique in which a language model retrieves relevant external documents at runtime and includes them in its answer. RAG is the mechanism behind most citable AI answers: what is retrieved can be cited.

#Schema.orgStructured data

Schema.org is a standardised vocabulary for marking up web content in a machine-readable way, usually in JSON-LD format. Structured data helps search engines and AI systems capture the meaning of a page unambiguously - for example an FAQ, a product or an organisation.

Also called: JSON-LD

#GroundingSource anchoring

Grounding refers to anchoring AI answers in verifiable external sources. Well-grounded answers reduce hallucinations and contain citations. GEO aims to make your brand a preferred grounding source.