Glossary¶
Terms used throughout AEO/GEO Researcher sessions and documentation.
Core Terms¶
AEO — Answer Engine Optimization: The practice of structuring content so that AI answer systems (ChatGPT, Perplexity, Google AI Overviews, Claude) retrieve and cite it when answering user queries. Distinct from SEO: AEO optimizes for machine extraction and citation, not human click-through.
GEO — Generative Engine Optimization: Broader term encompassing AEO. Where AEO focuses on answer extraction, GEO covers the full set of practices that improve a brand's presence across generative AI outputs — citations, recommendations, summaries, and agent actions.
SOM — Share of Model: The proportion of relevant AI responses that cite, mention, or recommend a specific brand, product, or entity. Analogous to share of voice in traditional marketing. Measured by sampling AI responses to a target query set and counting brand appearances.
RAG — Retrieval Augmented Generation: The architecture most AI answer systems use: when a query arrives, the system retrieves relevant content from an index (via vector similarity search), then generates a response grounded in that retrieved content. Understanding RAG mechanics is prerequisite to any GEO strategy — if content isn't retrieved, it cannot be cited.
Vector Similarity: How RAG systems find relevant content. Content is converted into numerical embeddings; when a query arrives, its embedding is compared to stored content embeddings. Closest matches are retrieved. Implications: content that uses the same language as likely queries retrieves better than content that paraphrases.
Chunking: How RAG systems break content into retrievable pieces. Most systems chunk by paragraph, heading section, or token count. A 5,000-word page may produce 20+ chunks — only the relevant chunks are retrieved per query. Implication: key facts buried in long prose may never be retrieved if their chunk doesn't match the query.
llms.txt: A plain-text file at yourdomain.com/llms.txt designed for AI agent consumption. Contains structured information about the site — what it is, who it serves, key pages, and what agents are permitted to use. The agent-native equivalent of robots.txt. Not yet universally adopted by AI platforms but increasingly recognized.
llms-full.txt: Extended version of llms.txt — contains the full site content in a flat, agent-readable format rather than linking to pages. Allows agents to read the entire site without crawling individual URLs.
AI Overview: Google's AI-generated response that appears above organic search results (formerly Search Generative Experience / SGE). Powered by a combination of Google's index and Gemini. Appearing in AI Overviews is a primary GEO target for content strategies targeting Google.
Citation: When an AI system includes a reference to a specific source in its response — either inline attribution or a source link. Citation is the primary measurable output of AEO/GEO work.
Entity: A recognized, named subject in AI knowledge graphs — a business, person, product, concept, or place that AI systems have learned to identify and distinguish from similar entities. Strong entity recognition (the AI knows who you are) is prerequisite to consistent citation.
JSON-LD: The structured data format preferred for schema markup. Placed in a <script type="application/ld+json"> block in the page <head>. AI crawlers that parse raw HTML can read it; AI tools that convert pages to markdown cannot (the script tags are stripped). This is the audit tool gap.
Schema Markup: Structured data vocabulary (schema.org) embedded in page HTML that explicitly labels content for machines. Types relevant to AEO: Organization, LocalBusiness, FAQPage, HowTo, Product, Review, Article, Speakable. Correct schema type selection matters — FAQPage markup on content that isn't actually FAQ format produces no signal lift.
MCP — Model Context Protocol: Anthropic's open standard for connecting AI models to external tools, data sources, and services. Relevant to agentic marketing: brands that expose MCP servers allow AI agents to take actions (book a table, check inventory, retrieve personalized data) rather than just retrieve information.
Agentic Marketing: Marketing designed for AI agents as the audience rather than (or in addition to) humans. Includes: llms.txt/llms-full.txt for agent readability, MCP servers for agent actions, structured data for agent extraction, and content framing optimized for how agents synthesize rather than how humans read.
Organic GEO vs Paid AI Placement: As of 2026, major AI platforms are integrating paid advertising alongside organic citations. Organic GEO = winning citations through content quality, entity authority, and technical signals. Paid AI placement = appearing in AI responses through advertising spend. These are distinct channels with different strategies. When a competitor appears in AI answers despite weak signals, paid placement is a plausible explanation.
OAI-SearchBot vs GPTBot: Two distinct OpenAI crawlers with different purposes. OAI-SearchBot powers ChatGPT's real-time web search (Bing-indexed content). GPTBot trains OpenAI models (future training data). Blocking GPTBot does not prevent ChatGPT search from finding your content — blocking OAI-SearchBot does.
Perplexity Bot: Perplexity's crawler. Aggressive crawl frequency relative to other AI crawlers. Content indexed by PerplexityBot is available for real-time citation in Perplexity responses. Blocking it removes content from Perplexity's citation pool.
Factual Density: The ratio of specific, verifiable facts to total content volume. High factual density = more citeable content per chunk. AI systems prefer dense, specific content for citation over prose that describes rather than states.