AEO & GEO Glossary: Answer Engine Optimization Terms
A plain-English glossary of Answer Engine Optimization (AEO) and Generative Engine Optimization (GEO) terms - from llms.txt and RAG to citation share and answer-first content.
This glossary defines the core concepts behind Answer Engine Optimization (AEO) and Generative Engine Optimization (GEO) - the discipline of getting your content cited and recommended by AI answer engines like ChatGPT, Perplexity, Google AI Overviews, Gemini, and Claude.
- Answer Engine Optimization (AEO)
- The practice of structuring content so AI answer engines - ChatGPT, Perplexity, Google AI Overviews, Gemini, and Claude - cite and surface it directly. Unlike SEO, which targets ranked blue links, AEO optimizes for being the quoted answer.
- Generative Engine Optimization (GEO)
- Often used interchangeably with AEO, GEO emphasizes getting recommended by generative AI engines that synthesize answers from multiple sources. It covers structured data, brand mentions across the web, and content an LLM can extract cleanly.
- Answer Engine
- An AI system that returns a synthesized, conversational answer instead of a list of links. Examples include ChatGPT Search, Perplexity, Google AI Overviews, and Gemini. Answer engines cite sources, making citation share the new ranking signal.
- The proportion of AI-generated answers for a topic that cite your domain as a source. It is the GEO equivalent of organic ranking position - the higher your citation share, the more often an engine recommends you.
- llms.txt
- A plain-text file at a site's root that gives AI crawlers a curated, machine-readable map of the most important pages and context. It is to answer engines what robots.txt and sitemap.xml are to traditional crawlers.
- Answer-First Content
- Writing that opens with a self-contained 40-60 word answer to the page's core question before any preamble. Answer engines lift the lede, so burying it behind an intro forfeits the citation.
- FAQ Schema (FAQPage)
- Structured data (JSON-LD) that marks up question-and-answer pairs. Answer engines extract FAQPage Q&A almost verbatim as cited answers, making it one of the highest-value AEO formats.
- Retrieval-Augmented Generation (RAG)
- An architecture where an AI engine retrieves relevant source documents at query time and grounds its answer in them, rather than relying only on parametric memory. Most answer engines use RAG, so being retrievable and quotable is what wins citations.
- Chunking
- How an answer engine splits a page into passages for retrieval. Content written in tight, self-contained paragraphs and clearly headed sections chunks cleanly, improving the odds a passage is retrieved and cited.
- Speakable Schema
- Schema.org markup that flags which parts of a page are best suited to text-to-speech and extraction. It gives voice assistants and answer engines an explicit hint about the most quotable content.
- Featured Snippet
- The boxed direct answer that traditional search shows above the results. Optimizing for featured snippets (concise definitions, tables, steps) overlaps heavily with AEO and is often a leading indicator of answer-engine citation.
- AI Crawler
- A bot that fetches pages for an AI engine - for training, indexing, or live retrieval when a user asks a question. Examples include GPTBot, ClaudeBot, PerplexityBot, ChatGPT-User, and Google-Extended. Allowing them in robots.txt is a prerequisite for being cited.
- Entity
- A person, organization, product, or concept that an engine recognizes as a distinct thing with attributes and relationships. Strong, consistent entity signals (schema, sameAs links, mentions) make an engine more confident citing you as an authority.
- Hallucination
- When an AI engine states something unsupported by any source. Well-structured, factual, citable content reduces the chance an engine fabricates an answer about your topic - and increases the chance it cites you instead.
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