Plain-English definitions for the terms behind Generative Engine Optimization and Google's quality framework — the working vocabulary I use in keynotes, audits, and client work.
How a brand or person earns visibility, citations, and trust inside AI answers.
The discipline of making a brand, person, or page reliably surface and get cited inside generative AI answers (ChatGPT, Google AI Overviews, Perplexity, Gemini, Copilot). GEO extends SEO from blue-link ranking to model-level recall and citation.
Optimizing content to be the direct answer surfaced by answer engines — featured snippets, voice assistants, and AI summaries. AEO is the precursor to and a subset of GEO.
A search interface that returns a synthesized answer instead of a list of links. Examples include Google AI Overviews, Perplexity, ChatGPT Search, and Gemini.
Google's generative answer block that appears above traditional results on eligible queries, citing a small set of source URLs. The single largest GEO surface by query volume in 2026.
A reference to a source URL or named entity inside an AI-generated answer. Citations are the new ranking — visibility without a citation is wasted exposure.
An architecture where a language model retrieves relevant documents from an index at query time and grounds its answer in them. RAG is the plumbing behind most production AI search products, which is why retrievability and chunk quality matter so much.
A numerical vector representation of text whose geometric distance encodes semantic similarity. Embeddings power how AI systems retrieve content for an answer — meaning your content competes on concepts, not just keywords.
Optimizing for the recognized things (people, products, brands, places) a search system understands, not just the strings of text. Entity SEO is what makes you visible as a concept, not just a keyword match.
A structured database of entities and their relationships, used by Google, Bing, and LLM training pipelines to disambiguate and connect facts. If you're not in one, you don't exist as an entity.
The open, collaboratively edited knowledge base behind Wikipedia. A Wikidata Q-identifier is one of the strongest single signals an entity exists in the AI knowledge stack — most major LLMs train on it directly.
A proposed plain-text file at a site's root that summarizes the canonical entity, key pages, and ground-truth facts for LLMs to ingest — the AI-era cousin of robots.txt and sitemap.xml. Not yet a standard, but cheap to deploy and increasingly read by AI agents.
A shared vocabulary (schema.org) typically embedded as JSON-LD that tells search engines and LLMs what a page is about in machine-readable form. Person, Organization, Article, FAQPage, Event, and DefinedTerm are the core types for GEO.
The percentage of AI-generated answers, across a set of buyer-intent prompts, in which your brand or person is cited or named. Tracked by tools like Semrush AI Toolkit, Profound, Otterly, and AthenaHQ.
A search session that ends on the results page without a click to any website, because the answer was delivered in the SERP or AI block. Zero-click is the dominant search behavior in 2026 — it forces brands to be valuable on-SERP, not just on-site.
User agents that fetch web content to train or ground language models. Key bots include GPTBot (OpenAI training), OAI-SearchBot (ChatGPT Search), ClaudeBot (Anthropic), PerplexityBot, and Google-Extended (Gemini training). Block or allow them in robots.txt deliberately.
Splitting a long document into smaller passages so a retrieval system can match the most relevant passage to a query. Pages that chunk cleanly — clear headings, self-contained paragraphs, one idea per section — get cited more often.
The signals human raters and ranking systems use to decide whether content deserves to win.
Google's Search Quality framework: Experience, Expertise, Authoritativeness, Trustworthiness. Used by human Quality Raters to evaluate content, and increasingly emulated by ranking systems and LLM trust signals. The first E (Experience) was added in December 2022.
Did the author actually do the thing they're writing about? First-hand, lived experience — product use, on-the-ground reporting, real client work — is the 2022 addition that separates AI-summarized content from human-grounded content.
The author's depth of knowledge in the topic, demonstrated by credentials, publications, and the precision of the content itself. Formal credentials matter for YMYL topics; demonstrated craft matters for everything else.
How recognized the author or site is as a go-to source for the topic — measured by citations from other authoritative sources, not by self-claim. Authority is earned externally and confirmed by who links to and quotes you.
The most important of the four. Is the page accurate, transparent about its author, safe, and honest about commercial relationships? Low trust caps the value of any expertise or authority you've built.
Pages that can materially affect a reader's health, finances, safety, or civic life. Held to the strictest E-E-A-T bar by Google's Quality Rater Guidelines.
Google's ~180-page handbook for the human contractors who rate sample queries. Not a ranking algorithm, but a public statement of what Google considers quality — and a blueprint for any GEO/EEAT strategy.
A clearly identified, machine-readable author — name, bio, photo, sameAs links to LinkedIn / Wikidata / X — attached to every piece of content. The strongest EEAT lever you fully control.
A schema.org property listing canonical URLs that identify the same entity across the web (LinkedIn, Wikidata, X, GitHub, etc.). The single highest-leverage line in a Person or Organization schema for disambiguation.
Helping a search system or LLM tell two same-named entities apart (e.g., Fernando Angulo the AI-search analyst vs. Fernando Angulo the footballer). Achieved via disambiguatingDescription, sameAs, and clear entity context on-page.
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