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AI Search & GEO Glossary

Plain-English definitions for the concepts behind AI visibility, citations, answer engines, and generative search.

01

AI Visibility

AI visibility is the degree to which AI answer engines (ChatGPT, Perplexity, Google AI Overviews, Claude) can find, understand, cite and recommend a brand when users ask relevant questions. Unlike search rankings, AI visibility is measured across mentions, citations and sentiment inside generated answers rather than positions on a results page. A site can rank well in classic search yet be invisible to AI engines if its content lacks crawl access for AI bots, machine-readable structure, or quotable evidence. In practice, teams measure AI visibility with three numbers: mention rate (how often the brand appears in sampled answers), citation rate (how often its pages are linked as sources), and sentiment (how the brand is described when it does appear). BrandGEO's audit adds a fourth, upstream layer — whether your site is even retrievable by the crawlers those engines depend on — because a brand cannot be recommended from pages the engines never saw.

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02

Generative Engine Optimization (GEO)

Generative Engine Optimization (GEO) is the practice of improving how often and how favorably AI answer engines cite and recommend your content. The term was popularized by the 2023 Princeton/Georgia Tech/IIT-Delhi paper "GEO: Generative Engine Optimization" (KDD 2024), which measured how content changes — added statistics, quotations, citations — raise a page's share in generated answers, reporting relative visibility improvements of up to 40% in its benchmarks. In practice GEO spans crawler access, structured data, evidence-dense writing, entity consistency and freshness signals. A working GEO program runs as a loop: audit what engines can see, fix access and structure, rewrite key pages around quotable evidence, then track mentions and citations to verify the changes moved the numbers. GEO complements rather than replaces SEO: search rankings remain one input into what AI engines retrieve, but citation-worthiness decides what they quote.

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03

Answer Engine Optimization (AEO)

Answer Engine Optimization (AEO) is optimizing content to be selected as the answer by engines that respond with a single synthesized reply — AI chatbots, voice assistants and featured snippets — rather than a list of links. AEO and GEO overlap heavily and are often used interchangeably; when distinguished, AEO emphasizes question-shaped content (FAQs, direct definitions, how-to steps) while GEO covers the broader pipeline including crawl access, schema and entity signals. In practice AEO work concentrates on three page patterns: definition blocks that answer "what is X" in the first eighty words, FAQ sections marked up with FAQPage schema, and step-by-step structures engines can lift wholesale. Teams typically treat AEO as the content-formatting layer inside a broader GEO program rather than a separate discipline with its own toolchain. If your buyers ask AI tools for recommendations, AEO/GEO determines whether you are in the answer.

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04

LLM SEO

LLM SEO is an informal umbrella term for making content discoverable and citable by large language model applications — chat assistants, AI search and agents. It covers the same ground as GEO/AEO: allowing AI crawlers, publishing machine-readable structure (JSON-LD, llms.txt), writing evidence-first content, and monitoring how LLM products describe your brand. The term appears in searches like "llm seo tools" and "llm seo meaning"; most practitioners now converge on GEO as the standard name for the discipline. Treat searches for LLM SEO as a naming signal rather than a separate method: the audience asking for it usually wants the same checklist — crawler access, llms.txt, structured data, evidence-first rewriting and answer tracking. The practical difference from classic SEO is where the work lands: instead of optimizing for a ranked list of links, you are optimizing to be retrieved, trusted and quoted inside a single synthesized answer.

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05

AI Citation

An AI citation is a source link or attribution inside an AI-generated answer — the pages Perplexity lists as sources, the links ChatGPT search shows, or the sites named in Google AI Overviews. Citations are the currency of AI visibility: engines cite pages that are retrievable, parseable and contain specific, quotable evidence (numbers, definitions, comparisons). Citation behavior differs by engine: Perplexity cites nearly every claim inline, Google AI Overviews link a rotating subset of supporting pages, and ChatGPT search shows sources for browsed answers. The common thread is that engines prefer pages where a specific claim can be lifted with its evidence attached — which is why evidence-dense sections, named statistics and clearly attributed quotes consistently out-earn general marketing copy. Tracking which of your pages earn citations — and which competitors get cited instead — is the core feedback loop of GEO work.

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06

Share of Voice in AI

Share of voice in AI is the percentage of AI-generated answers about your category that mention or cite your brand versus competitors. It adapts the classic media metric to answer engines: sample the questions your buyers ask, run them across engines, and count whose products appear. Because AI answers concentrate on a few sources, share of voice tends to be winner-take-most — which is why early GEO investment in a niche can lock in outsized visibility. To make the metric operational, fix three variables before you measure: the prompt set (real buyer questions, not vanity keywords), the engine list, and the sampling cadence — AI answers vary run to run, so single-shot checks mislead. Reported movement should always come with its prompt set attached; a share-of-voice number without the underlying questions is marketing, not measurement.

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07

Prompt Tracking

Prompt tracking is monitoring a fixed set of questions (prompts) across AI engines over time to measure brand visibility: who gets mentioned, cited and recommended for each prompt. It is the AI-era equivalent of rank tracking. A useful prompt set mirrors real buyer questions ("best X for Y", "X alternative", "is X worth it") rather than vanity keywords. A workable starter set is 20-50 prompts spanning four intents: category recommendations, direct comparisons, problem questions your product solves, and brand verification ("is X legit"). Because engines answer stochastically, track each prompt across repeated runs and report trends rather than single answers — a brand that appears in three of five runs this month and five of five next month is the signal you're looking for. Movement in prompt tracking data — after shipping fixes or content — is the main evidence that GEO work is paying off.

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08

Query Fan-Out

Query fan-out is the process where an AI engine expands one user question into multiple internal sub-queries, retrieves results for each, then synthesizes a single answer. Google's AI Mode publicly describes this technique. Fan-out means your page can be cited for a question it never targeted verbatim — the engine may pull you in via a sub-query about pricing, a definition or a comparison. The practical implication: a comparison page can be cited for a pricing sub-query, and a glossary definition can be pulled into a buying-guide answer. Structuring pages so each section answers one self-contained question — with its own heading, evidence and definition — turns a single URL into many fan-out targets, which is why section-level structure matters more in GEO than page-level keyword targeting did in classic SEO.

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09

llms.txt

llms.txt is a proposed plain-text file at a site's root (like robots.txt) that gives large language models a curated, markdown-formatted map of the site's most important content. Proposed by Jeremy Howard in 2024 (llmstxt.org), it lets sites present clean, token-efficient context to AI systems instead of forcing them to parse full HTML. The format is deliberately simple: an H1 with the project name, a short summary, then curated markdown links grouped by section; a companion llms-full.txt can carry expanded content. Adoption is voluntary and engine support varies, so treat llms.txt as a complement to — not a replacement for — robots.txt, sitemaps and schema: engines that ignore it lose nothing, and engines that read it get your best pages in clean form. Publishing one is a low-cost way to control the narrative AI models see first.

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10

GPTBot

GPTBot is OpenAI's web crawler, identified by the GPTBot user-agent, used to collect publicly available web content that may improve OpenAI's models. Sites control it via robots.txt: allowing GPTBot makes content available for model training and related uses; OpenAI also operates separate agents (like OAI-SearchBot for ChatGPT search) with distinct user-agent strings and purposes, per OpenAI's published bot documentation. For GEO, the practical decision is which OpenAI bots to allow: blocking GPTBot while allowing OAI-SearchBot keeps you out of training data but visible in ChatGPT search results. Verify real GPTBot traffic before drawing conclusions from server logs: OpenAI publishes its IP ranges, and spoofed user-agents are common. The robots.txt decision is also reversible — many publishers allow search-facing bots while restricting training crawlers, and revisit the split quarterly as OpenAI's bot roster changes.

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11

AI Overview

AI Overviews are Google's AI-generated summaries shown above classic results for many queries, synthesizing information from multiple sources with links. They matter for GEO because they compress the results page: studies across 2024-2026 consistently measure lower click-through to organic links when an AI Overview is present. Appearing inside the Overview (as a cited source) recovers some of that lost visibility — which requires the same crawlability, structure and citability work as other answer engines. For measurement, separate two questions: how often Overviews appear for your target queries (trigger rate), and how often you are cited when they do (inclusion rate). Trigger rates shift with Google's rollouts and vary sharply by query type — informational queries trigger far more often than navigational ones — so a traffic drop should be diagnosed against both numbers before blaming content.

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12

IndexNow

IndexNow is an open protocol (indexnow.org) that lets websites instantly notify participating search engines — including Bing and Yandex — when URLs are added, updated or deleted, instead of waiting for recrawls. A site hosts a key file and POSTs changed URLs to the API. Implementation is deliberately light: generate a key, host the key file at your site root, and POST changed URLs to api.indexnow.org — one request can carry up to 10,000 URLs. Google does not participate; it relies on sitemaps and its own crawl scheduling. For GEO, IndexNow matters because Bing's index feeds Microsoft Copilot and is one of the retrieval sources in the ChatGPT search stack — fast Bing indexing shortens the path from publish to AI-citable. The protocol only signals that a URL changed; participating engines still decide whether and when to crawl and index it, so IndexNow shortens discovery, not evaluation.

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