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Your comprehensive guide to Generative Engine Optimization and AI visibility terminology. Master the language of AI search optimization.
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Fundamental GEO and AI visibility concepts
AI visibility measures how often your brand is mentioned, cited, or recommended in AI-generated responses from ChatGPT, Perplexity, Google AI Overviews, and other LLM platforms.
An answer engine is an AI-powered search system like Perplexity, ChatGPT, or Google AI Overviews that delivers synthesized direct answers instead of traditional lists of links.
Generative Engine Optimization (GEO) is the practice of optimizing content to get cited and recommended by AI search engines like ChatGPT, Perplexity, and Google AI Overviews.
Major AI assistants and search engines
ChatGPT is OpenAI's AI assistant with hundreds of millions of users, making it one of the most important platforms for brand visibility in AI-powered search and recommendations.
Claude is Anthropic's AI assistant known for nuanced reasoning, safety-focused design, and 200K-token context windows, with growing enterprise adoption making it important for GEO.
Gemini is Google's multimodal AI model family powering AI Overviews, Gemini chat, and Workspace integrations — central to AI search visibility across the Google ecosystem.
Google AI Overviews are AI-generated summaries atop Google search results that synthesize answers from multiple sources, reshaping organic search visibility for brands.
Microsoft Copilot is an AI assistant powered by GPT models and Bing search, integrated into Windows and Microsoft 365 — a growing platform for enterprise AI visibility.
Perplexity AI is an AI-powered search engine that provides direct answers with numbered source citations, making it a key platform for AI attribution and referral traffic.
Key performance indicators and scores
AI attribution is how platforms like Perplexity, ChatGPT, and Google AI Overviews credit, cite, and link to original source content when generating responses to user queries.
AI Visibility Score is a 0-100 rating that measures how well-optimized your content is for discovery, citation, and recommendation by AI search engines and large language models.
Citation tracking monitors when and how AI platforms like ChatGPT, Perplexity, and Google AI Overviews reference, quote, or link to your brand and content in their responses.
Content Structure Score measures how well a webpage is organized with heading hierarchies, lists, and semantic formatting that AI systems can easily parse and cite.
Share of Voice (SOV) in AI search measures the percentage of AI-generated brand mentions you receive versus competitors across ChatGPT, Perplexity, and Google AI Overviews.
Zero-click search occurs when users get answers directly from AI Overviews, featured snippets, or AI chat responses without clicking through to any website.
Technical implementation and infrastructure
AI crawlers are automated bots like GPTBot, ClaudeBot, and PerplexityBot used by AI companies to discover and index web content for LLM training and real-time retrieval systems.
AI hallucination occurs when LLMs like ChatGPT or Gemini generate plausible-sounding but factually incorrect information about brands, products, or facts — a key reputation risk.
A knowledge graph is a structured database of interconnected entities and facts used by Google, ChatGPT, and Perplexity to verify information and understand entity relationships.
A Large Language Model (LLM) is an AI system trained on massive text datasets that powers ChatGPT, Claude, Gemini, and AI search engines like Perplexity.
llms.txt is a proposed web standard file placed at your domain root that gives AI language models verified, structured facts about your organization to reduce hallucinations.
Multimodal AI systems like GPT-4 Vision, Gemini, and Claude 3 process text, images, audio, and video — requiring GEO strategies that optimize visual and media content for AI.
Retrieval Augmented Generation (RAG) is an AI technique that retrieves real-time web content before generating responses, powering citation-based platforms like Perplexity.
Schema markup is structured data code (JSON-LD) added to HTML that helps search engines and AI systems understand the context, meaning, and relationships within your web content.
Semantic HTML uses meaningful elements like article, section, and header to describe content structure, enabling AI systems and search engines to better extract information.
Optimization approaches and best practices
Authority signals are indicators like author credentials, publication dates, citations, and schema markup that help AI systems assess content credibility and trustworthiness.
Content freshness measures how recently a webpage was published or updated, directly influencing how AI systems like ChatGPT and Perplexity prioritize sources for citation.
Content velocity is the rate at which a website publishes new or updated content, influencing topical authority and how AI search engines perceive site relevance.
E-E-A-T (Experience, Expertise, Authoritativeness, Trustworthiness) is Google's content quality framework that also influences how AI systems select and cite sources.
Entity optimization structures content around clearly defined named entities — people, organizations, products, and places — so AI systems can accurately identify and cite them.
Featured snippets are highlighted answer boxes at position zero in Google search results that often serve as source content for AI Overviews, influencing AI citation selection.
Prompt engineering is the practice of crafting effective AI queries to elicit desired responses, helping GEO practitioners optimize content for real user search behavior.
A prompt library is a curated set of AI search queries used to systematically monitor brand mentions, citations, and competitive visibility across AI platforms.
Topical authority is a website's demonstrated expertise depth in a specific subject area, directly influencing how often AI systems like ChatGPT and Perplexity cite it as a source.
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