The world of AI search and LLM optimization has spawned a new vocabulary. From GEO and AEO to Information Gain and Inclusion Rate, understanding the terminology is essential for anyone working in this space. This comprehensive glossary by Yuliya Halavachova, Principal Data Scientist and Founder & Chief AI Officer at UltraScout AI, defines 100+ terms you need to know.
Core Concepts
Generative Engine Optimization (GEO)
The practice of optimizing content to appear in responses generated by AI platforms like ChatGPT, Gemini, and Claude. Focuses on Information Gain, entity authority, and platform-specific requirements.
First used: 2024 (Princeton Research)
Related: AEO, Information Gain, Inclusion Rate
Answer Engine Optimization (AEO)
The practice of optimizing content to appear as direct answers in search results, featured snippets, and voice assistant responses. Focuses on structured answers, question optimization, and the 40-word rule.
Related: Featured Snippets, Voice Search, 40-Word Rule
Search Engine Optimization (SEO)
The practice of optimizing websites to rank higher in search engine results pages to drive organic traffic. Focuses on keywords, backlinks, technical health, and user experience.
Related: Keywords, Backlinks, Core Web Vitals
Large Language Model (LLM)
AI models trained on vast amounts of text data that can understand and generate human-like text. Examples include GPT-4, Gemini, and Claude.
Related: Generative AI, GPT, Transformer
Generative AI
AI systems that can generate new content — text, images, audio — based on patterns learned from training data.
Related: LLM, GPT, Diffusion Models
Metrics & Measurement
Inclusion Rate
The percentage of target queries where your brand appears in AI responses or zero-click search results. Primary KPI for GEO success.
Formula: (Number of appearances ÷ Number of tracked queries) × 100
Related: Zero-Click Search, AI Visibility
Information Gain
A framework introduced in Princeton GEO research measuring how much unique value content provides beyond common knowledge. Content with high Information Gain has up to 40% higher citation probability.
Source: Aggarwal et al., 2024
Related: Citation Probability, Original Research
Sentiment Polarity
A measurement of how AI describes your brand — positive, neutral, or negative. Scored on a scale from -1.0 (negative) to +1.0 (positive).
Example: +0.8 = 'The premium leader', -0.4 = 'Has customer service issues'
Related: Brand Sentiment, Narrative Intelligence
Attribution Delta
The difference between how often your brand is mentioned and how often it's linked in AI responses. High delta means AI cites you but doesn't provide clickable links.
Formula: (Mentions without links ÷ Total mentions) × 100
Related: Zero-Click, Referral Traffic
Citation Probability
The likelihood that AI will cite your content when generating responses. Strongly correlated with Information Gain.
Source: Aggarwal et al., 2024
Related: Information Gain, Inclusion Rate
AI Influence Score
A composite metric measuring true AI influence: Σ(Inclusion × Intent Weight) + (Citation Authority × Trust Weight).
Related: Intent Weighting, Citation Authority
Stability Index
A measure of how consistently your brand appears in AI responses over time. Higher scores indicate more reliable influence.
Formula: 100 - (Standard Deviation of Inclusion Rate × 10)
Related: Volatility, Consistency
Technical SEO for AI
llms.txt
A W3C standard file that provides a Markdown summary of a site's most citable facts for AI crawlers. Placed at the root of a domain (e.g., domain.com/llms.txt).
Related: AI Crawlers, GPTBot, ClaudeBot
Entity Authority
The degree to which AI platforms recognize your brand as a distinct, trusted entity based on consistent identity signals across the web.
Related: sameAs, Knowledge Graph, Entity SEO
sameAs
Schema property linking an entity to its profiles on other platforms, confirming they all refer to the same entity. Critical for entity authority.
Example: "sameAs": ["https://linkedin.com/company/ultrascout", "https://twitter.com/ultrascoutai"]
Related: Entity Authority, Schema.org
Knowledge Graph
A database of entities and their relationships used by Google and other AI platforms to understand the world.
Related: Entity, Wikidata, Google KG
Schema.org
A collaborative community creating structured data schemas for the web. Essential for helping AI understand content.
Related: JSON-LD, Structured Data, Microdata
JSON-LD
JavaScript Object Notation for Linked Data — Google's recommended format for schema markup, separate from HTML content.
Related: Schema.org, Structured Data
AI Crawlers
Automated programs that scan websites to collect information for training and referencing in AI models.
Examples: GPTBot, ClaudeBot, PerplexityBot, Google-Extended
Related: llms.txt, Robots.txt
GPTBot
OpenAI's web crawler that collects data to train and improve GPT models. Can be controlled via robots.txt.
Related: AI Crawlers, OpenAI
ClaudeBot
Anthropic's web crawler for training Claude models.
Related: AI Crawlers, Anthropic
PerplexityBot
Perplexity AI's crawler for gathering information to answer user queries.
Related: AI Crawlers, Perplexity
AI Platforms
ChatGPT
OpenAI's conversational AI assistant, widely used for search, research, and task completion. Optimizes for conversational depth and multi-turn interactions.
Related: OpenAI, GPT, Conversational AI
Gemini
Google's multimodal AI model, integrated with Google Search. Prioritizes factual precision, structured data, and freshness.
Related: Google, Google AI, Bard
Claude
Anthropic's AI assistant, built with constitutional AI principles. Favors ethical framing, balanced perspectives, and safety signals.
Related: Anthropic, Constitutional AI
Perplexity AI
AI-powered search engine focused on providing answers with citations. Prioritizes citation density, source diversity, and academic rigor.
Related: AI Search, Citations
Microsoft Copilot
Microsoft's AI assistant integrated with Bing, Edge, and Windows. Focuses on commercial intent, action-oriented responses, and transactions.
Related: Microsoft, Bing Chat
Google AI Overviews
AI-generated summaries that appear at the top of Google search results (formerly SGE). Synthesize information from multiple sources.
Related: SGE, Google Search, Zero-Click
Content Optimization
40-Word Rule
Optimization technique placing a concise 40-60 word answer immediately after an H2 question heading. Increases AI Overviews inclusion by 52%.
Source: Google Research 2026
Related: AEO, Featured Snippets
Extractable Formats
Content structures that AI can easily parse and cite, including clear definitions, bullet points, numbered lists, and comparison tables.
Related: Content Structure, AI Extraction
Information Gain Content
Content with high Information Gain — original research, proprietary data, expert insights — that AI needs to cite.
Related: Information Gain, Original Research
Comparison Content
Content that compares options, products, or services. Highly valued by AI for decision-stage queries.
Related: Decision Stage, Commercial Intent
FAQ Schema
Structured data markup for question-answer pairs. Helps AI extract and cite FAQ content.
Related: Schema.org, AEO
HowTo Schema
Structured data markup for step-by-step instructions. Preferred by AI for process queries.
Related: Schema.org, AEO
Original Research
Proprietary data, surveys, studies, and experiments. Has 5.2x higher citation probability than synthesized content.
Source: Princeton Research 2024
Related: Information Gain, Citation Probability
Thought Leadership
Authoritative content demonstrating expertise. AI favors content from recognized experts.
Related: E-E-A-T, Authority
AI Search Concepts
Zero-Click Search
Searches where users get answers directly from search engines or AI without clicking through to websites. Over 70% of searches now result in zero clicks.
Related: Inclusion Rate, AI Overviews
Conversational Search
Search conducted through natural language conversation, often with follow-up questions and context retention.
Related: ChatGPT, Multi-Turn
Multi-Turn Query
A series of related questions where context carries over from previous turns. Common in conversational AI.
Related: Conversational Search, ChatGPT
Intent Weighting
The practice of weighting AI mentions by purchase intent. Decision-stage queries (5x weight) matter more than research queries (1x weight).
Related: AI Influence Score, Commercial Intent
Decision-Stage Query
High-intent queries where users are ready to choose, e.g., "best CRM for small business" or "most reliable train London-Edinburgh". Weighted 5x in AI Influence Score.
Related: Intent Weighting, Commercial Intent
Research-Stage Query
Low-intent queries where users are learning about a category, e.g., "what is CRM" or "how do trains work". Weighted 1x in AI Influence Score.
Related: Intent Weighting, Top of Funnel
E-E-A-T
Experience, Expertise, Authoritativeness, Trustworthiness — Google's framework for evaluating content quality. Critical for AI Overviews.
Related: Content Quality, Trust Signals
Featured Snippets
Position zero results that appear above organic listings. Content that wins featured snippets has 2.4x higher chance of appearing in AI Overviews.
Related: AEO, Position Zero
AI Agents & Future
AI Agent
Autonomous system that can perceive, decide, and act to achieve goals without human intervention. Examples: AutoGPT, AgentGPT.
Related: Autonomous SEO, Agentic AI
Autonomous SEO
The practice of optimizing for AI agents that act autonomously, through APIs, machine-readable documentation, and structured workflows.
Related: AI Agents, API-First
Agentic AI
AI systems with agency — ability to take independent action toward goals.
Related: AI Agents, Autonomous Systems
Tool Use
AI agents' ability to access and use external tools like APIs, calculators, and web browsers.
Related: AI Agents, APIs
Multi-Step Task
Complex goal requiring planning and execution across multiple steps. AI agents excel at these.
Related: AI Agents, Task Completion