Glossary

Key terms and definitions for AI search optimization, Generative Engine Optimization (GEO), and AI ranking analysis.

A

AI Answer Presence
A metric measuring whether AI models recognize and can provide information about a specific brand when asked directly. A brand with high AI Answer Presence is known to most major AI models; a brand with low presence is essentially invisible in the AI knowledge base.
AI Brand Mention
An instance where an AI model references a specific brand in its generated response. AI brand mentions are valuable because they carry high trust with users and can directly influence purchasing decisions. Monitoring AI brand mentions is a key component of GEO strategy.
AI Interpretation Quality
A metric measuring how accurately AI models describe what a business does. High interpretation quality means AI models correctly identify your products, services, target audience, and value proposition. Low quality means AI models may misrepresent your business.
AI Overviews
AI-generated summaries that appear at the top of Google search results for certain queries. Powered by Google's Gemini model, AI Overviews provide direct answers to user questions within the search results page, reducing the need to click through to individual websites.
AI Ranking
A measure of how prominently and accurately a brand or website is represented in AI-generated responses. Unlike traditional search rankings that assign a numerical position, AI ranking reflects whether a brand is mentioned at all, how frequently it appears across different AI models, and how accurately it is described.
AI Retrieval Likelihood
A metric measuring the probability that AI models will mention a specific brand when asked about its category. For example, if a user asks ChatGPT for the best CRM tools, AI Retrieval Likelihood measures whether your CRM product is included in the response.
AI SEO
An umbrella term for optimizing a brand's online presence for both traditional search engines and AI-powered search systems. AI SEO encompasses traditional SEO practices plus GEO-specific strategies like monitoring AI brand mentions, building category association, and ensuring accurate AI interpretation of your brand.
AI Visibility
The degree to which a brand, product, or website is known and accurately represented by AI language models. High AI visibility means that when users ask AI assistants about your category or brand, you are mentioned prominently and described correctly.

B

Brand Authority
The perceived expertise, trustworthiness, and prominence of a brand within its industry. In the context of AI search, brand authority is determined by the breadth, quality, and consistency of a brand's mentions across the web, which directly influences how AI models represent the brand.

C

Category Association
The strength of the connection between a brand and its product category in AI model knowledge. Strong category association means AI models reliably recommend your brand when users ask about your category. It is built through consistent co-occurrence of your brand name with category keywords across multiple authoritative sources.

F

Fine-Tuning
The process of further training a pre-trained language model on specific data to improve its performance for particular tasks. Fine-tuning can affect how AI models represent brands and categories, and different fine-tuning approaches are one reason why ChatGPT, Claude, and Gemini may give different recommendations.

G

Generative Engine Optimization (GEO)
The practice of optimizing a brand's online presence to improve visibility in AI-generated responses. GEO is the AI equivalent of SEO and involves content optimization, authority building, and monitoring across multiple AI platforms. The term was formalized in a 2023 research paper by Princeton, Georgia Tech, Allen AI, and IIT Delhi.

K

Knowledge Cutoff
The date after which an AI model has no training data. Information published after the knowledge cutoff may not be reflected in the model's responses unless it uses real-time retrieval (RAG). Different AI models have different knowledge cutoffs, which is one reason they may have different awareness of newer brands.

L

Large Language Model (LLM)
A type of artificial intelligence model trained on massive amounts of text data that can understand and generate human-like text. Examples include GPT-4 (powering ChatGPT), Claude, Gemini, and DeepSeek. LLMs are the technology behind AI search and AI-generated recommendations.

M

Model Bias
The tendency of AI models to favor certain brands, products, or perspectives based on patterns in their training data. Model bias can cause some brands to be over-represented and others to be under-represented in AI recommendations, regardless of their actual quality or relevance.

P

Prompt Engineering
The practice of crafting effective inputs (prompts) for AI models to get desired outputs. In the context of AI ranking analysis, prompt engineering determines how questions are asked to AI models to accurately assess a brand's visibility and representation.

R

Retrieval-Augmented Generation (RAG)
A technique where AI models supplement their training data by retrieving relevant documents from the web or a knowledge base before generating a response. RAG allows AI models to provide more accurate, up-to-date answers and is increasingly used by ChatGPT, Gemini, and other AI assistants.

S

Structured Data
Standardized code (typically schema.org markup) added to web pages to help search engines and AI systems understand the content. Common types include Organization, Product, FAQ, and Review schemas. Structured data improves both traditional SEO and AI retrieval by making content machine-readable.

T

Training Data
The massive dataset of text used to train a large language model. Training data typically includes web pages, books, academic papers, forums, and other text sources. A brand's presence in AI training data directly influences whether and how AI models represent that brand in their responses.

Z

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