AI Search Glossary
Key terms and concepts in AI search optimization and generative engine optimization (GEO).
AI Overviews
Google's AI-powered feature that synthesizes answers directly on the search results page. AI Overviews reduce click-through to external websites by providing users with an immediate answer, often pulling from multiple sources without sending traffic to any of them.
Citation-Worthy Claims
Specific data points, original research, or unique frameworks in your content that AI models are more likely to reference and cite in generated responses. Generic content that restates widely available information gets filtered out; original, data-backed claims get cited.
Entity Recognition
The AI capability to identify and understand distinct entities — brands, people, organizations, products — mentioned in text. When AI models recognize your brand as a distinct entity with clear attributes, they are more likely to recommend it in relevant contexts.
FAQ-Based Content
Question-and-answer formatted content that maps directly to how users interact with AI chatbots and assistants. LLMs pull heavily from FAQ-style content because it directly matches user query patterns, giving businesses with comprehensive FAQ pages a significant advantage in AI search visibility.
Generative Engine Optimization (GEO)
The practice of structuring your online presence so that large language models like ChatGPT, Perplexity, and Gemini are more likely to reference, recommend, and cite your brand. Unlike traditional SEO which optimizes for search engine crawlers, GEO optimizes for the AI models that synthesize information into narrative responses.
Large Language Models (LLMs)
AI models such as GPT-4, Claude, and Gemini that process and generate natural language. Unlike traditional search engines that rank pages, LLMs synthesize information from multiple sources into a single narrative response, fundamentally changing how users discover brands and products.
Retrieval-Augmented Generation (RAG)
A system where an AI model supplements its training data with real-time retrieval from external sources like the web or databases. RAG enables AI tools like Perplexity to provide current, sourced information rather than relying solely on the model's training data cutoff.
Schema Markup
Structured data code (such as FAQ schema, product schema, or how-to markup) added to web pages that provides clear extraction paths for AI models and search engines. Schema markup helps AI tools understand and extract information from your content more reliably.
Structured Content
Content organized with clear headers, lists, direct answers to questions, and logical hierarchies. AI models can more easily parse and cite structured content compared to long-form marketing copy or walls of unformatted text.
Zero-Click Searches
Search queries where the user gets their answer directly from the search engine or AI tool without clicking through to any website. The rise of AI Overviews and chatbot-based search has dramatically increased zero-click searches, reducing organic traffic even for sites with strong Google rankings.