Essential terminology for AI search optimization and LLM marketing.
Google's AI-generated summary that appears at the top of some search results. Formerly called Search Generative Experience (SGE). Shows AI-synthesized answers with source citations.
A metric measuring how often and prominently a brand is mentioned by AI systems for relevant queries. Calculated by testing query sets across platforms and scoring mention frequency, position, and context.
The credibility a source gains from being mentioned and referenced by other authoritative sources. AI systems use citation patterns to evaluate trustworthiness.
The percentage of relevant queries where AI cites a particular brand or source. A key GEO performance metric.
The amount of text an LLM can process in a single interaction. Affects how much retrieved content can be considered when generating responses.
Experience, Expertise, Authoritativeness, Trustworthiness. Google's quality evaluation framework that also applies to AI systems' source evaluation.
Numerical representations of text that capture semantic meaning. Used by AI systems for semantic search—finding content based on meaning rather than exact keyword matches.
AI's ability to identify and understand specific entities (brands, people, places, concepts). Established entities are more likely to be cited and recommended.
The practice of optimizing content, brands, and digital presence to improve visibility and citations in AI-generated responses. The AI-era equivalent of SEO.
OpenAI's web crawler that indexes content for ChatGPT's knowledge base. Can be controlled via robots.txt.
A database of entities and their relationships. Google's Knowledge Graph and similar systems help AI understand and verify entity information.
AI systems trained on massive text datasets to understand and generate human language. Examples include GPT-4, Claude, and Gemini.
Paid advertising within AI interfaces, such as ChatGPT's sponsored placements launching in 2026.
Name, Address, Phone consistency across all online platforms. Critical for local GEO as inconsistencies reduce AI trust.
Perplexity AI's web crawler. Indexes content for Perplexity's search-first AI assistant.
The input or query a user provides to an AI system. Understanding how users prompt AI informs content optimization.
A technique that combines LLMs with real-time information retrieval. Enables AI to access current information beyond its training data.
Where a brand appears in AI's list of recommendations. First position is more valuable than later positions.
Search based on meaning rather than exact keywords. AI systems use semantic search to find conceptually relevant content.
A brand's AI visibility relative to competitors. Calculated as the percentage of category citations that mention a specific brand.
Structured data that helps AI understand content meaning. Types include Organization, Person, Product, FAQ, and others.
The text corpus used to train an LLM. Content in training data influences AI's baseline knowledge about topics and entities.
The perceived expertise a source has on a specific topic. Built through comprehensive, accurate content coverage.
A database optimized for storing and searching embeddings. Used in RAG systems to find semantically similar content.
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