GEO Glossary
Authoritative definitions of key Generative Engine Optimization, AI search, and citation analysis terms.
GEO (Generative Engine Optimization)
The discipline of optimizing web content to be cited by generative search engines like ChatGPT, Claude, Perplexity, and Google AI Overviews.
Unlike classic SEO which targets position #1 on Google, GEO aims to have AI recommend your content when a user asks a question. The practice includes adding explicit definitions, structured schema.org data, direct-answer snippets, and competitive positioning in plain text that AI can extract.
Related: AI Search · Citation Score · Schema.org
AI Search
Search mediated by artificial intelligence — the user asks a chatbot (ChatGPT, Claude, Perplexity) and receives a synthesized response with citations, rather than a list of links.
In 2026, more than 30% of high-intent searches (purchases, comparisons, technical problems) already go through AI search. The key difference from traditional search is that the user doesn't click 10 results — they receive ONE synthesized response that cites 3-5 sources. If your site isn't one of those sources, you don't exist for that user.
Related: AI Overviews · Citation Score
AI Overviews
Generative response blocks that Google shows above organic results when it detects a question. Formerly known as SGE (Search Generative Experience).
They function as an AI layer over Google's SERP: they read the top pages for your query, synthesize a response, and cite sources inline. If your content appears as an AI Overview source, you receive highly qualified traffic. If not, users tend not to scroll down to classic organic results.
Related: AI Search · Citation Score
Citation Score
A site's visibility metric in AI engines — the percentage of plausible queries where the site appears cited in the responses of the queried AI engines.
At AICite we calculate the score by generating 15 representative queries from the URL's content, running them through each configured AI engine, and measuring the citation ratio. Score 0/100 = never cited. Score 100/100 = cited in every query × every engine. Most indie sites score 0-20/100; established unicorns reach 50-90/100 depending on the engine.
Related: GEO · LLM-as-judge
LLM-as-judge
An AI engineering pattern where one LLM evaluates another LLM's output against structured criteria — useful for classifying, extracting information, or detecting properties in non-deterministic responses.
At AICite we use Claude Haiku 4.5 as judge to extract whether an AI engine's response cites a given site: it reads the text + sources, decides cited (yes/no), position, sentiment (positive/neutral/negative), and lists competitors cited. It's the modern replacement for regex-and-hope when classifying text. It works because models like Haiku are cheap ($1/$5 per million tokens) and consistent at structured tasks.
Related: Multi-engine orchestration · Citation Score
Multi-engine orchestration
The pattern of running multiple LLMs in parallel for a single task, aggregating results or using consensus.
AICite queries ChatGPT and Claude (and optionally Perplexity, AI Overviews) in parallel with Promise.allSettled — if one engine fails, the others continue, and we report the successful subset. Each engine has different biases (Claude knows B2B SaaS companies better, ChatGPT has broader general coverage), so running them together gives a more complete view of visibility.
Related: LLM-as-judge · Cross-engine asymmetry
Cross-engine asymmetry
The phenomenon where the same site has very different scores across different AI engines (e.g., 93% on Claude but 20% on ChatGPT).
Each AI engine has different training data, prioritizes different types of content, and uses different retrieval strategies. Sites well established in a specific niche (e.g., Linear in project management) can be well known by one engine and nearly invisible in another. Detecting this asymmetry is one of the first things GEO enables — before optimizing, measure per engine separately.
Related: GEO · Citation Score
Hallucination
When an LLM generates incorrect information with a tone of confidence — e.g., inventing product features, misattributing a quote, confusing entities with similar names.
Particularly damaging in GEO because your site may be cited but described incorrectly. Real audited example: ChatGPT believes Staxly (a dev platform catalog) is a Minecraft proxy. The typical cause is ambiguous content or missing disambiguation signals — no clear "About" section, or niche terms not defined. GEO fixes include detecting and correcting specific hallucinations.
Related: GEO · Disambiguation
Disambiguation signals
Explicit cues in your content that help AI correctly classify your site: "X is a tool for Y targeting Z users", "X competes with A, B, C", JSON-LD SoftwareApplication.
Without disambiguation signals, AI tends to infer incorrectly — especially for ambiguous brand names or products in new categories. Good signals include: an explicit one-liner definition in the H1 or opening paragraph, schema.org structured data, explicit comparisons with well-known competitors, and "What we're NOT" sections for edge cases.
Related: Hallucination · Schema.org
Schema.org
A standardized vocabulary of data types (Article, FAQPage, SoftwareApplication, etc.) inserted as JSON-LD in HTML so that AI crawlers and search engines can extract structured information without ambiguity.
In GEO it's particularly useful for: marking visible FAQs (FAQPage), declaring pricing and category (SoftwareApplication), describing comparisons (Product), and providing breadcrumbs. AI engines use it for both retrieval and citations — a FAQ marked with FAQPage is much more likely to be cited than free-form prose with the same information.
Related: Disambiguation signals · GEO
Want to know how visible your site is to AI engines?
Audit your URL for free →