What the AI Answerability Index Measures

The Complete Measurement Framework

The AI Answerability Index represents years of research into how AI systems select, process, and cite source content. The 106 individual checks emerged from analyzing patterns in AI citation behavior, studying how language models parse and understand content, and testing which factors most strongly predict whether content receives accurate citations.

The seven dimensions organize these checks into logical categories that correspond to different aspects of content quality. Some dimensions focus on content itself, examining what you write and how you structure it. Other dimensions focus on technical factors, examining how your content is marked up and delivered. Together, they provide a complete picture of your content's AI readiness.

Understanding what each dimension measures helps you interpret your scores and prioritize improvements. A low score in one dimension requires different corrective action than a low score in another. The following sections detail every dimension and explain its constituent checks.

Parseability: Structure and Organization

Parseability measures how easily AI systems can identify and extract the structural elements of your content. When AI processes a page, it must first understand the document's organization before it can extract specific information. Content with strong parseability allows AI to quickly locate and isolate relevant sections.

Heading Hierarchy (Checks 1 through 6)

These checks examine your use of HTML heading elements from H1 through H6. They verify that you have exactly one H1 element containing your primary topic, that headings follow proper hierarchical order without skipping levels, and that headings accurately describe the content that follows them.

Proper heading hierarchy matters because AI systems use headings to understand content organization and navigate to specific sections. When headings are missing, duplicated, or improperly nested, AI may misunderstand your content structure or fail to locate specific information.

Content Segmentation (Checks 7 through 12)

These checks evaluate how effectively you divide content into logical sections. They examine paragraph length distribution, the presence of clear section breaks, and whether related content is grouped together. Optimal segmentation balances readability with information density.

Well-segmented content is easier for AI to chunk for processing. AI systems often break content into segments for analysis, and clear segmentation ensures these chunks align with your intended meaning rather than arbitrarily splitting your ideas.

List and Table Structure (Checks 13 through 18)

These checks assess your use of lists and tables for presenting structured information. They verify that lists use proper HTML markup, that items within lists are parallel in construction, and that tables include appropriate headers and captions.

Lists and tables signal structured information that AI can extract efficiently. When comparing products, presenting steps, or organizing data, proper list and table markup helps AI understand the relationships between items.

Clarity: Directness and Precision

Clarity measures how clearly your content conveys its message. AI systems perform better with content that communicates directly and avoids ambiguity. The clarity dimension examines writing style, vocabulary choices, and the precision of your statements.

Sentence Structure (Checks 19 through 26)

These checks analyze sentence construction including length, complexity, and grammatical clarity. They identify overly long sentences that may confuse parsing, excessive use of passive voice, and complex nested clauses that obscure meaning.

Straightforward sentence structure reduces the likelihood of AI misinterpretation. When extracting facts for answers, AI systems work more reliably with clear, direct statements than with complex sentences requiring multiple parsing passes.

Vocabulary and Terminology (Checks 27 through 33)

These checks examine your word choices including consistency of terminology, appropriate use of technical language, and clarity of definitions. They identify when terms are used inconsistently or when jargon appears without explanation.

Consistent vocabulary helps AI maintain entity coherence throughout your content. When you refer to the same concept with different terms, AI may not recognize the connection, fragmenting your content's authority on that topic.

Statement Precision (Checks 34 through 40)

These checks evaluate whether your statements are specific enough to be useful. They identify vague claims, unsupported generalizations, and statements that lack necessary context or qualification.

Precise statements are more citable than vague ones. AI systems prefer to cite specific facts, figures, and well-defined claims rather than general observations that could come from many sources.

Entity Authority: Identity and Expertise

Entity Authority measures how well your content establishes identity and expertise for the entities it discusses. AI systems need to understand who is speaking, what they are qualified to discuss, and how entities relate to each other.

Entity Identification (Checks 41 through 48)

These checks verify that key entities including people, organizations, products, and concepts are clearly identified. They examine whether entities have explicit names, whether their roles are specified, and whether identifying information is consistent throughout the content.

Clear entity identification enables AI attribution. When AI cites your content, it needs to attribute information to specific sources. Unclear entity identification makes accurate attribution difficult or impossible.

Expertise Signals (Checks 49 through 55)

These checks evaluate signals that establish expertise and authority. They look for author credentials, organizational qualifications, citations of authoritative sources, and evidence of first-hand knowledge or experience.

Expertise signals affect AI's trust assessment of your content. AI systems are increasingly sophisticated at evaluating source reliability. Content with strong expertise signals is more likely to be cited for authoritative answers.

Entity Relationships (Checks 56 through 62)

These checks examine how well you define relationships between entities. They look for explicit connections between authors and organizations, products and manufacturers, concepts and their broader contexts.

Entity relationships help AI build knowledge graphs from your content. Well-defined relationships enable AI to understand not just individual entities but how they connect to form coherent concepts and topics.

Question Readiness: Answering User Queries

Question Readiness measures how well your content answers the types of questions users actually ask. AI systems select citations based on how directly content addresses user queries. Content optimized for question readiness is structured to provide direct answers.

Query Alignment (Checks 63 through 70)

These checks evaluate whether your content addresses common questions in your topic area. They examine whether you include question-oriented headings, whether answers are clearly stated, and whether you cover the range of questions users might ask.

Query-aligned content is more likely to appear in AI responses. When AI systems receive questions, they search for content that directly addresses those questions. Content structured around common queries has a natural advantage.

Funnel Coverage (Checks 71 through 78)

These checks assess whether your content addresses questions across the awareness funnel. Top-of-funnel questions are informational and exploratory. Middle-of-funnel questions compare options. Bottom-of-funnel questions drive decisions. Complete coverage ensures relevance across user intents.

Funnel coverage expands your citation opportunities. Different users ask different types of questions. Content that only addresses one funnel stage misses citations for questions at other stages, even when you have relevant expertise.

Answer Completeness (Checks 79 through 84)

These checks verify that when you address a question, you provide complete answers. They examine whether answers include necessary context, whether related follow-up questions are anticipated, and whether answers are self-contained.

Complete answers earn more prominent citations. AI systems prefer sources that fully address questions rather than sources that provide partial information requiring supplementation from other sources.

AI Accessibility: Non-Text Content

AI Accessibility measures how effectively AI systems can process non-text content on your pages. Images, videos, and other media require special attention to be accessible to AI systems that primarily process text.

Image Descriptions (Checks 85 through 89)

These checks evaluate whether images have appropriate alt text and descriptions. They verify that descriptions are specific rather than generic, that they convey the image's informational content, and that decorative images are properly marked.

Proper image descriptions extend your content's reach. AI systems increasingly process images, but text descriptions remain important for ensuring the information in images contributes to your answerability.

Media Alternatives (Checks 90 through 93)

These checks assess whether video and audio content has text alternatives. They look for transcripts, captions, and text summaries that convey the content's information in text form.

Media alternatives make multimedia content citable. Information locked in video or audio without text alternatives is invisible to most AI systems. Providing alternatives dramatically expands the usable content on your pages.

Schema Completeness: Structured Data

Schema Completeness measures your implementation of structured data markup. Schema provides explicit, machine-readable information that helps AI systems understand your content without ambiguity.

Schema Presence (Checks 94 through 98)

These checks verify that relevant schema types are present. Different content types warrant different schema: articles should have Article schema, products should have Product schema, organizations should have Organization schema.

Missing schema forces AI to infer information that could be stated explicitly. While AI can often make correct inferences, explicit schema removes uncertainty and ensures accurate understanding.

Schema Validity (Checks 99 through 102)

These checks validate that your schema markup is syntactically correct and follows Schema.org specifications. Invalid schema may be ignored entirely by AI systems, negating your markup efforts.

Valid schema is foundational for machine readability. Syntax errors, missing required properties, or incorrect data types can all cause AI systems to discard your structured data.

Schema Completeness (Checks 103 through 106)

These checks evaluate whether your schema includes all relevant properties, not just required ones. Comprehensive schema with optional properties provides richer information for AI consumption.

Complete schema maximizes your structured data value. Each additional property provides another data point that AI systems can use for understanding, classification, and citation.

Crawl Health: Technical Access

Crawl Health measures technical factors affecting AI crawler access to your content. The best content is worthless for AI citation if technical issues prevent AI systems from fully accessing and processing your pages.

Response Codes and Performance (Checks covered in this dimension)

These checks verify that your pages return proper HTTP response codes, load within acceptable time limits, and do not trigger error states. They also check for proper handling of redirects and canonical URLs.

Technical access is a prerequisite for all other factors. AI systems that cannot access your pages cannot cite them, regardless of how well your content scores in other dimensions.

Mobile and Rendering Compatibility

These checks assess whether your pages render properly across different environments. They verify mobile compatibility, JavaScript handling, and proper viewport configuration.

Compatibility ensures consistent access across AI systems. Different AI crawlers may access your pages in different ways. Broad compatibility ensures your content is accessible to all major AI platforms.