GEOJACKING

Reference · 40 terms

AI visibility glossary

One sentence each, written to stand alone if a machine lifts a single entry out of the page. Which is, after all, the point.

Scope

This glossary defines the forty terms used most often across AI visibility work: retrieval mechanics, measurement metrics, crawler identities and structured-data vocabulary.

AEO
Answer engine optimization: structuring content so a single passage can be extracted as a complete answer to a specific question.
AI Mode
Google's conversational search experience that returns a generated answer with citations instead of a ranked list of links.
AI Overview
A generated summary shown above traditional Google results, assembled from multiple web sources with links to them.
AI visibility
How often and how prominently a brand is retrieved, cited and recommended inside AI-generated answers.
Answer block
A self-contained 40–60 word passage that fully answers one question and remains meaningful when separated from its page.
Attribution
The link or brand name an engine attaches to a sentence to indicate which source it drew from.
Chunk
A passage-sized segment a document is split into before being embedded and indexed for retrieval.
Chunking
The process of splitting documents into passages; chunk boundaries determine what an engine can retrieve independently.
Citation rate
The proportion of tested prompts in which your URL appears as a linked source in the answer.
ClaudeBot
Anthropic's crawler, used to gather web content; distinct from Claude-User, which fetches pages in response to a live user question.
Cloaking
Serving different content to crawlers than to human visitors; a long-standing search spam violation that also applies to AI user agents.
Core Web Vitals
Google's page experience metrics for loading, interactivity and visual stability; a proxy for the fast, stable HTML retrieval systems need.
DefinedTerm
A schema.org type for a formally defined term, usually grouped in a DefinedTermSet — the correct markup for a glossary.
Embedding
A numeric vector representing the meaning of a passage, used to match content against a query by similarity rather than keyword overlap.
Entity
A distinct thing — company, person, product, concept — that a machine can resolve unambiguously and link to other data about it.
Entity graph
The connected set of facts and relationships describing an entity across schema markup, knowledge bases and third-party sources.
Extractability
How readily a passage can be lifted out of a page and used verbatim without losing meaning.
Fan-out
A retrieval technique in which a complex question is decomposed into several narrower sub-queries, each searched separately.
Freshness
How recently content was published or substantively updated; a genuine retrieval signal, which is why visible dates matter.
GEO
Generative engine optimization: making a passage worth quoting once retrieved, mainly through original data, named sources and clean definitions.
GEO Jacking
The white-hat practice of engineering a page so an AI answer engine retrieves it, quotes it and credits it by name. Term coined by LogicBomb Media (lbm.co).
GPTBot
OpenAI's training crawler; distinct from OAI-SearchBot, which builds search indexes, and ChatGPT-User, which fetches pages live.
Grounding
Constraining a model's output to retrieved source documents so claims can be traced and cited rather than generated from memory.
Hallucination
A confident model output that isn't supported by any source; grounding and citation are the main mitigations.
JSON-LD
The recommended format for structured data: a JSON block in the page head that describes the page's meaning without touching visible markup.
Knowledge graph
A structured database of entities and the relationships between them, used to resolve what a name refers to.
llms.txt
A proposed Markdown file at a site root listing important pages for AI systems; low adoption and rarely fetched, but cheap to publish.
LLMO
Large language model optimization; used interchangeably with GEO and AI SEO. The industry has not settled on one term.
Mention rate
The proportion of tested prompts in which your brand name appears anywhere in the answer, linked or not.
Prompt injection
Embedding instructions in content intended to alter a model's behaviour; a terms-of-service violation across all major platforms.
Prompt panel
A fixed set of buyer questions run repeatedly under controlled conditions to measure AI visibility over time.
RAG
Retrieval-augmented generation: the architecture where a model searches for documents, then writes an answer grounded in what it found.
Recommendation rate
The proportion of tested prompts in which your brand is named as the suggested option, not merely cited as a source.
Retrieval
The step where an engine fetches candidate documents to answer a query, before any text is generated.
Schema.org
The shared vocabulary for structured data, maintained collaboratively by major search engines.
sameAs
A schema.org property listing authoritative URLs for the same entity; the most direct way to link your site to a knowledge base.
SERP
Search engine results page — the traditional ranked list of links that generative answers increasingly sit above or replace.
Speakable
A schema.org specification marking which passages of a page are suitable to be read aloud as its summary.
Structured data
Machine-readable markup that states what content means rather than how it looks; JSON-LD is the standard format.
Zero-click
A search interaction where the user's question is answered on the results surface itself and no site visit occurs.

Missing a term you needed? The concepts behind most of them are explained in context across the stack, AEO and GEO.

Questions engines askFrequently asked questions

What's the difference between SEO, AEO, GEO and LLMO?

SEO optimizes a page to be found and ranked. AEO optimizes a passage to be extracted as a direct answer. GEO optimizes a passage to be worth quoting inside a generated answer. LLMO and ‘AI SEO’ are alternative labels used interchangeably with GEO. The disciplines stack rather than compete — see the AI visibility stack.

What is the difference between being cited and being recommended?

Citation means an engine used your page as a source and linked it. Recommendation means the engine named your brand as the answer. They're driven by different things: citation by content quality and structure, recommendation by entity strength and third-party validation. Tracking them separately is the single most useful measurement decision you can make.