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Glossary

The GEO + AI Search Glossary.

Plain-language definitions for the 27 terms you'll hear constantly when Generative Engine Optimization comes up. Each term links to a deeper page with mechanism, practical impact, and the CiterLabs perspective.

All terms
  • Generative Engine Optimization (GEO)
    Generative Engine Optimization (GEO) is the practice of structuring a brand's content, entity footprint, and third-party signals so that AI engines like ChatGPT, Perplexity, Claude, and Google AI Overviews cite that brand inside their generated answers.
  • Citation Share
    Citation share is the percentage of relevant prompts in which an AI engine cites a specific brand or domain inside its generated answer.
  • llms.txt
    llms.
  • llms-full.txt
    llms-full.
  • Entity Strength
    Entity strength is how well a brand exists as a named, recognizable entity across structured public sources like Wikipedia, Wikidata, Crunchbase, GitHub, and authority graphs.
  • Answer-Ready Content
    Answer-ready content is content structured so individual passages can be lifted intact and used as standalone answers by AI engines.
  • Third-Party Signals
    Third-party signals are mentions, reviews, listicles, and citations of a brand on websites the brand does not control — Reddit, podcasts, YouTube, listicle pages, news, and community sites.
  • Schema Markup
    Schema markup is structured data added to web pages using vocabularies like Schema.
  • FAQPage Schema
    FAQPage schema is a Schema.
  • Google AI Overviews
    Google AI Overviews are AI-generated answer summaries that appear at the top of Google search results for many queries, often satisfying the user without a click.
  • ChatGPT Search
    ChatGPT Search is the search-augmented mode of ChatGPT, launched in late 2024, that retrieves and cites web sources alongside generated answers.
  • Perplexity AI
    Perplexity AI is an AI answer engine that combines large language models with real-time web search and inline citations.
  • Claude
    Claude is the family of large language models built by Anthropic, used in the Claude.
  • GEO vs SEO
    SEO optimizes for ranking on a search results page.
  • robots.txt
    robots.
  • JSON-LD
    JSON-LD is a JSON-based serialization format for Linked Data, commonly used to embed Schema.
  • Knowledge Graph
    A knowledge graph is a structured representation of entities and the relationships between them.
  • Retrieval-Augmented Generation (RAG)
    Retrieval-Augmented Generation (RAG) is an AI architecture where a model retrieves relevant external content at query time and uses it to generate an answer.
  • Topical Authority
    Topical authority is the depth and breadth of content a site has on a specific subject area.
  • Pillar Page
    A pillar page is a long, comprehensive article that covers a topic in depth, serves as the central hub for a topic cluster, and links to multiple supporting pages.
  • Content Cluster
    A content cluster is a group of related pages on a website organized around a central pillar topic, all interlinked.
  • Programmatic SEO
    Programmatic SEO is the practice of automatically generating large numbers of pages targeting long-tail keywords, typically by combining a page template with a structured dataset.
  • E-E-A-T (Experience, Expertise, Authoritativeness, Trust)
    E-E-A-T is Google's framework for evaluating content quality based on the demonstrated Experience, Expertise, Authoritativeness, and Trustworthiness of the source.
  • Embeddings
    Embeddings are numerical vector representations of text, used by AI systems to measure semantic similarity.
  • Vector Database
    A vector database is a specialized database that stores and retrieves embeddings (high-dimensional vectors).
  • Freshness Signals
    Freshness signals are indicators that content is current — recent dates, updated pricing, current competitor names, recent statistics.
  • GEO Audit
    A GEO audit is a structured assessment of a brand's current citation share across AI engines, identifying gaps in entity strength, content structure, third-party signals, schema, and freshness — and prioritizing remediation.

Reading definitions is the easy part. Getting cited is the hard part.

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