AEO & GEO glossary
Every term that matters in AI search — defined, with a link to the full explainer where one exists. New entries land here as we ship them.
Concepts
The foundational ideas — start here if you're new to AEO.
AEO (Answer Engine Optimisation)
Getting your brand named, cited and recommended inside AI answers from ChatGPT, Claude, Perplexity, Gemini and Google AI Overviews.
GEO (Generative Engine Optimisation)
The technical and content layer that makes a site readable, parseable and trustworthy to generative AI systems.
AEO vs SEO
Where blue-link optimisation ends and answer-inclusion optimisation begins — and why the same site needs both.
AI search vs SEO
Where SEO measures ranking in a list of blue links, AI search measures whether your brand is named and cited in the synthesised answer. Different user behaviour, different metrics.
Google AI surfaces
Google's two AI search products — and the structural differences that shape how you measure visibility on each.
How each engine works
Engine-by-engine breakdowns of how citations get picked and why some brands consistently win.
How ChatGPT chooses citations
ChatGPT chooses citations by running a web search via its tool-use system, then re-ranking results through its own relevance model before generating the answer.
How Claude Web Search works
Claude's web search is a tool-use capability where the model decides when to query the web, what to query for, and which results to use as evidence. Often runs multiple search hops per answer.
How Gemini grounding works
Gemini grounding lets the model anchor answers in real web sources via Google's groundingMetadata. When enabled, Gemini returns the answer alongside a structured list of source URLs.
How Google AI Overviews pick sources
Google AI Overview picks sources via a retrieval pipeline that overlaps with classical Google ranking but weights different signals — particularly structured data, freshness, and passage extractability.
How Perplexity ranks sources
Perplexity is a search-native AI — every answer is grounded in live web sources with inline numbered citations. It weights freshness, factual density, and primary-source signals more heavily than other AI engines.
Metrics
The four numbers that matter when you measure your AI visibility.
Brand hallucination prevention
When AI engines fabricate false claims about your brand — invented features, wrong pricing, made-up partnerships. Caused by information vacuums where authoritative content is missing.
Citation rate (AI search metric)
Percentage of AI answers that include your domain in the source citation list. Measures who the AI trusts as evidence.
Share of voice in AI answers
Your brand visibility relative to the other brands the AI mentions in the same answer set. The most defensible AEO metric — normalises for query selection bias.
Visibility (AI search metric)
Percentage of AI answers that name your brand in the response prose. Measures who the AI recommends to the buyer.
Tactics
What you actually do to move the metrics above.
AEO checklist
The 20-item priority list — foundations, content, entity signals, ongoing tactics. Work in order.
What is llms.txt?
The emerging markdown manifest standard for AI crawlers, similar to robots.txt for search.
AEO tools comparison
Honest comparison of monitoraeo, Otterly.ai, Profound, Athena, Goodie and others.
llms.txt examples + templates
Real-world examples of llms.txt files from sites that publish them, plus a working template you can copy. Updated as the standard evolves.
Structured data for AI engines
Structured data (JSON-LD) tells AI engines exactly what your page is about in a format they can parse unambiguously. The schemas that move the needle for AI citations are different from the schemas that win SEO rich results.
See it on your own brand
The fastest way to understand any of these terms is to see them measured against your own domain. The free preview gives you a real-data snapshot in under a minute.