Glossary
AEO Glossary
The definitive reference for answer engine optimization terminology. Every term links to the Amisora feature or tool that measures and improves it.
Definitions
Core AEO terminology
These are the key terms used in answer engine optimization. Each definition links to the Amisora feature that directly measures or improves that signal.
- Answer Engine Optimization (AEO)
- The practice of structuring web content so that AI-powered search engines—like ChatGPT, Perplexity, Google AI Overviews, and Microsoft Copilot—can discover, understand, and cite it in their generated responses. AEO is an optimization layer that works alongside traditional SEO, not a replacement for it.
- Related: What Is AEO? · AEO Strategy Guide
- AI Visibility
- A measure of how discoverable and prominent your content is within AI-generated search results. AI visibility tracks whether AI engines can find, process, and reference your pages when responding to relevant queries. Amisora scores AI visibility as one of seven audit dimensions.
- Measured by: Seven-Dimension Scoring · AI Visibility Audit
- Answerability
- The degree to which a page’s content directly and clearly answers specific questions. Pages with high answerability lead with concise, factual responses and use question-based headings that match how users query AI engines. Low answerability means the page discusses a topic but never provides a clear, extractable answer.
- Measured by: Answerability Score · AEO Audit Tool
- Citation Readiness
- How prepared a page is to be quoted or referenced by an AI engine in a generated answer. Citation-ready content includes clear attributions, verifiable facts, specific data points, and clean semantic structure. A page can be highly answerable but still fail at citation readiness if it lacks the trust signals AI engines need to confidently reference it.
- Measured by: Citation Readiness Score · How to Get Cited in AI Search
- Entity Clarity
- The precision with which a page defines the people, products, organizations, and concepts it references. High entity clarity helps LLMs map your content to their internal knowledge graphs and attribute information correctly. Poor entity clarity leads to AI engines confusing your brand with competitors or misattributing your content.
- Measured by: Entity Clarity Score · LLM Visibility Tool
- Extractability
- How easily AI systems can pull specific passages, facts, or data points from your content. Extractable content uses clean heading hierarchy, ordered and unordered lists, tables, and short paragraphs that can be isolated without losing meaning. Walls of unstructured text score low on extractability.
- Measured by: Extractability Score · AEO Audit Tool
- Ambiguity Risk
- The likelihood that AI engines will misinterpret, skip, or avoid citing your content due to vague language, unsupported claims, or contradictory statements. Low ambiguity risk means every claim is specific, sourced, and unambiguous. AI engines penalize ambiguous content because citing it would reduce the accuracy of their generated answers.
- Measured by: Ambiguity Risk Score · AEO Audit Tool
- Brand Signals
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On-page and schema-level indicators that help AI engines identify and correctly attribute your brand. Strong brand signals include consistent naming across all pages, Organization schema markup, social profile links (
sameAs), and clear product-brand associations. Weak brand signals cause AI engines to attribute your content to competitors or generic sources. - Measured by: Brand Signals Score · AEO for SaaS · AEO for Ecommerce
- Citation Rate
- The frequency with which AI search engines reference or quote your content when answering relevant queries. Citation rate is an emerging KPI that measures your brand’s share of voice in AI-generated answers. Unlike click-through rate in traditional SEO, citation rate tracks whether your content is being used as an authority source even when users don’t visit your site.
- Related: AI Search Optimization · LLM Visibility Tool
- LLM Visibility
- The extent to which large language models (LLMs) like GPT, Claude, and Gemini can access, understand, and reference your content. LLM visibility is influenced by content structure, schema markup, crawlability, and semantic clarity. It differs from traditional search visibility because LLMs evaluate content through semantic understanding rather than keyword matching.
- Measured by: LLM Visibility Tool · AI Visibility Audit
- Retrieval-Augmented Generation (RAG)
- A technique where AI engines retrieve relevant documents from the web or a knowledge base, then use those documents as context when generating an answer. RAG is how most AI search engines (Perplexity, Google AI Overviews, ChatGPT with browsing) decide which sources to cite. Optimizing for RAG means ensuring your content is both discoverable (retrievable) and clearly structured (usable as generation context).
- Related: What Is AEO? · AI Search Optimization
- Generative Engine Optimization (GEO)
- A broader term sometimes used to describe optimization for any generative AI system. GEO and AEO overlap significantly, with AEO being the more established and specific term for optimizing content for AI search citation. In practice, most strategies labeled “GEO” use the same techniques as AEO: structured content, schema markup, entity clarity, and semantic precision.
- Related: What Is AEO? · AEO Strategy Guide
See these metrics in action
Run an AEO audit on any page to see your answerability, extractability, citation readiness, entity clarity, and ambiguity risk scores - with specific fixes for each.