This glossary defines the key terms in AI search optimization, from AEO and GEO to AI Overviews, structured data and query fan-out. As search shifts from ranked links to AI-generated answers, a new vocabulary has emerged, and understanding it is the first step to competing in it. Each term below includes a clear, plain-language definition you can use as a reference. For a deeper comparison of the three core approaches, see our guide to AEO vs. GEO vs. SEO.
Answer Engine Optimization (AEO)
AEO is the practice of structuring content so an answer engine cites and recommends it inside a direct answer, rather than simply listing it in results. It builds on traditional SEO and adds direct answers, question-based structure, statistics, citations and structured data that AI systems rely on.
Generative Engine Optimization (GEO)
GEO is answer engine optimization aimed specifically at generative AI engines, the tools that compose original answers rather than returning links. It overlaps heavily with AEO and emphasizes how large language models select, synthesize and attribute sources when they generate a response.
Search Engine Optimization (SEO)
SEO is the practice of improving a website so it ranks higher in the unpaid results of a search engine like Google. It combines technical health, on-page relevance, quality content and earned authority, and it remains the foundation that AEO and GEO build upon.
Answer Engine
An answer engine is any system that responds to a query with a direct, composed answer rather than a list of links. This includes AI chatbots like ChatGPT and Perplexity and AI-powered features inside traditional search, such as Google AI Overviews.
AI Overviews
AI Overviews are AI-generated summaries that appear at the top of Google search results for many queries, composed from multiple sources and shown above the traditional links. Being cited in an AI Overview places your brand in front of searchers before they scroll to the ranked results.
AI Mode
AI Mode is Google’s dedicated conversational search experience, where users ask questions and receive AI-generated answers with citations. Google reported it passed 1 billion monthly users in 2026, making it a major and growing surface for AI visibility.
Large Language Model (LLM)
A large language model is the type of AI that powers modern answer engines, trained on vast amounts of text to understand and generate human-like language. LLMs underpin tools like ChatGPT, Gemini and the AI features inside search.
Retrieval-Augmented Generation (RAG)
RAG is a technique where an AI engine retrieves relevant documents from the web or a knowledge base and uses them to ground its generated answer. It is a key reason well-structured, authoritative web content can be cited in AI responses.
Grounding
Grounding is the process of tying an AI engine’s answer to real, retrievable sources rather than relying only on its training data. Grounded answers cite the pages they draw from, which is why being a citable source matters for AI visibility.
Citation (in AI search)
A citation is when an AI engine references, quotes or links to your content as a source inside its answer. Earning citations is the core goal of answer engine optimization, and citation share is how AI visibility is measured.
Entity
An entity is a distinct, identifiable thing such as a brand, person, product or place that engines recognize and connect to other information. Clear entity signals help AI engines understand who your brand is and confidently associate it with your category.
Structured Data (Schema Markup)
Structured data is code, usually JSON-LD from Schema.org, that labels the meaning of your content so machines can parse and attribute it. Types like FAQPage, HowTo and Organization make content more extractable for AI answers.
Featured Snippet
A featured snippet is a short, direct answer Google pulls from a page and displays at the top of traditional results. Optimizing for featured snippets, with answer-first content, overlaps closely with optimizing for AI answers.
Query Fan-Out
Query fan-out is when an AI engine breaks a single question into several related sub-questions, searches each, and synthesizes the results into one answer. Content built around clear, question-based sections is more likely to be pulled into these fanned-out answers.
Zero-Click Search
A zero-click search is one where the user gets their answer directly on the results page or in an AI answer without clicking through to a website. This trend makes being named or cited in the answer itself more important than ever.
Topical Authority
Topical authority is the depth and credibility a site demonstrates on a subject, built through thorough, interlinked content. Both search rankings and AI citations reward topical authority, which is why pillar-and-cluster content structures work.
Share of Voice (in AI search)
Share of voice in AI search is how often your brand is named or cited by AI engines for your key topics, relative to competitors. It is the brand-level measure of AI visibility, tracked by querying engines and recording who appears.
llms.txt
An llms.txt file is a proposed standard, similar in spirit to robots.txt, that gives AI systems a curated, machine-friendly guide to a site’s most important content. It is an emerging way to help engines find and understand your key pages.
E-E-A-T
E-E-A-T stands for Experience, Expertise, Authoritativeness and Trustworthiness, the quality signals Google uses to assess content. Strong E-E-A-T, including named authors, credentials and citations, also helps AI engines decide which sources to trust and cite.
Conversational Search
Conversational search is asking search tools questions in natural, back-and-forth language rather than typing short keywords. AI engines are built for it, which rewards content written the way people actually ask questions.
Semantic Search
Semantic search interprets meaning and intent rather than matching exact keywords. It underpins both modern SEO and AI answers, which is why clear, topically thorough content outperforms keyword-stuffed pages.
Knowledge Graph
A knowledge graph is a structured database of entities and the relationships between them that engines use to understand the world. Being represented accurately in knowledge sources helps AI engines recognize and describe your brand correctly.
Prompt
A prompt is the question or instruction a user gives an AI engine. The way buyers phrase prompts shapes which content gets surfaced, so understanding the common prompts in your category helps you build content that answers them directly, in the language buyers actually use.
FAQ
What is the difference between AEO and GEO?
AEO optimizes to be cited as the answer across answer engines generally, while GEO focuses specifically on generative AI engines like ChatGPT and Gemini. They overlap so heavily that most teams treat them as one discipline.
What is the difference between AEO and SEO?
SEO optimizes to rank your page in a list of results; AEO optimizes to have your content cited as the answer an engine gives. They share a content foundation, but AEO adds direct answers, structure and citations.
Why do these terms matter for my business?
Because search is shifting from ranked links to AI-generated answers, and the brands that understand and optimize for this new landscape earn visibility while others quietly disappear from the results buyers now rely on.
Is AEO replacing SEO?
No. AEO builds on SEO rather than replacing it. The same authoritative, well-structured content that ranks in search is what AI engines cite, so the two work together.