AI Visibility Glossary

Clear definitions of key terms used in AI visibility, answer engine optimization, and generative search ranking.

AEO (Answer Engine Optimization)

The practice of optimizing content to be discovered, understood, and cited by pure AI answer engines like ChatGPT, Claude, and Perplexity. Unlike traditional SEO which targets search result rankings, AEO focuses on becoming a preferred source for AI-generated answers. Key factors include structured data, answerability, and crawler access.

AI Visibility

A metric measuring how often and how accurately AI systems reference your brand, products, or content when generating answers. High AI Visibility means LLMs frequently cite you; low visibility means you're absent from AI-generated responses. Analogous to "SEO visibility" but for generative engines.

Answerability

The degree to which your content directly answers user questions in a clear, structured format. Highly answerable content uses question-based headings, FAQ structures, and "answer first" formatting. AI models strongly favor answerable content for citations.

Citation Coverage

The percentage of relevant queries where AI systems cite your domain. Calculated as: (queries where you're cited) ÷ (total relevant queries tested). Higher coverage indicates stronger AI visibility across your topic space.

Crawlability

The technical accessibility of your site to AI training crawlers (GPTBot, Claude-Web, etc.). Factors include robots.txt permissions, XML sitemaps, internal linking structure, and server response times. Poor crawlability = zero AI visibility.

Entity Graph

A network of structured relationships between entities (people, organizations, products, places) expressed via schema markup. Example: Your Organization connects to your Products, which connect to Reviews. Complete entity graphs help AI models understand your brand's full context.

FAQPage Schema

A specific type of JSON-LD structured data that marks up frequently asked questions and their answers. Considered the highest-impact schema for AI visibility because it directly maps to how users query AI assistants. Format: Question → Answer pairs with explicit markup.

GEO (Generative Engine Optimization)

Optimizing for traditional search engines with AI features, primarily Google AI Overviews and Bing Copilot. GEO sits between classic SEO and pure AEO—targeting hybrid systems that combine web search with generative AI answers.

Grounding

The process where AI models connect generated answers to real web sources to verify accuracy and provide attribution. Models that use grounding (like Perplexity) are more likely to cite specific domains. Non-grounded models (early ChatGPT) generate from training data without real-time web access.

GPTBot

OpenAI's web crawler used to train GPT models and ground ChatGPT responses. Identified by User-Agent: GPTBot. Blocking it in robots.txt prevents your content from being used in training or cited in answers.

LLM (Large Language Model)

AI systems trained on vast amounts of text to understand and generate human-like language. Examples: GPT-4, Claude, Llama. LLMs power modern AI assistants and are increasingly used as search interfaces, making LLM visibility critical for discovery.

Render Gap

The difference between static HTML and JavaScript-rendered content. If critical information only appears after JavaScript executes, some AI crawlers will miss it. Measured as: content visible to JS-disabled crawlers vs. full rendered page. Large render gaps hurt AI visibility.

Schema Markup

Structured data vocabulary (schema.org) embedded in pages to help machines understand content relationships. Implemented via JSON-LD, Microdata, or RDFa. Critical for AI visibility because it eliminates ambiguity in entity identification, content type, and factual claims.

Semantic Search

Search that understands query intent and meaning rather than just keyword matching. AI assistants use semantic understanding to connect user questions with relevant content, even when exact keywords don't match. Requires clear, conceptual content over keyword-optimized text.

Source Attribution

When AI-generated answers include explicit citations or links to the sources used. High-quality attribution names your domain and provides a clickable link. Poor attribution mentions generic "sources" without specifics. Optiview tracks both types.

Structured Data

Information organized in a machine-readable format using standardized vocabularies (schema.org). Contrasts with unstructured data (plain text). Structured data enables AI models to parse facts, relationships, and entities with high confidence, dramatically improving citation likelihood.