AEO Glossary

An ask engine is an AI-powered system that generates direct answers to user questions rather than displaying a list of ranked web pages. Ask engines evaluate content based on clarity, structure, and trustworthiness.

Ask Engine Optimization (AEO) is the practice of structuring digital content so it can be accurately understood, extracted, and cited by AI systems when responding to user questions.

AEO prioritizes answers over rankings and focuses on clarity, neutrality, and authority.

→ Read more: What Is Ask Engine Optimization (AEO)?

AI market share refers to the percentage of AI-generated answers within a topic or industry that reference or rely on a specific brand, website, or content source.

Unlike keyword market share, AI market share measures answer visibility, not traffic volume.

An AI citation occurs when an AI system references a website, brand, or source as part of a generated answer. Citations may be visible (linked) or implicit (used without attribution).

Content extraction is the process by which AI systems identify, isolate, and reuse specific portions of a webpage—such as definitions, steps, or explanations—to generate answers.

Structured content improves extraction accuracy.

An entity is a distinct, identifiable concept, organization, person, or object that AI systems recognize as a stable reference point within a knowledge graph.

Examples include companies, technologies, locations, and defined concepts.

Entity-based search is an approach used by AI systems to understand relationships between concepts rather than relying solely on keywords.

Entities help AI systems connect related topics and verify contextual accuracy.

Google AI Overview is an AI-generated summary that appears in Google search results, providing direct answers to user queries by synthesizing information from multiple sources.

A knowledge graph is a structured database used by AI systems to store information about entities and their relationships.

Knowledge graphs help AI verify facts, connect concepts, and determine authoritative sources.

A large language model (LLM) is an AI system trained on extensive datasets to understand and generate human-like language.

LLMs power many ask engines and AI search interfaces.

Schema markup is structured data added to web pages to help search engines and AI systems understand content context, relationships, and meaning.

Schema supports improved content interpretation but does not guarantee AI citation.

Source eligibility describes whether content meets the criteria required for AI systems to safely reference it when generating answers.

Eligibility depends on clarity, neutrality, structure, and topical authority.

Topical authority refers to the degree to which a website consistently demonstrates expertise and depth on a specific subject.

AI systems prefer sources that cover topics comprehensively rather than superficially.