Definition Pattern Detection
"What is AEO?" -14% of all AI queries start with "What is." If your content doesn't answer with a clean definition sentence, someone else's will.
Questions this article answers
- ?How do I write definitions that AI engines will extract for "What is" queries?
- ?What sentence patterns make content more likely to appear in AI answers?
- ?Where should I place definitions on a page for maximum AI citation?
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Quick Answer
Definition patterns are sentence structures like "[Term] is [definition]" or "[Term] refers to [explanation]" that AI systems extract for direct-answer responses. In our testing, pages with clear definition patterns are roughly 3x more likely to appear in "What is..." queries.
Before & After
Before - Definition buried in narrative
When businesses want to improve how they handle customer interactions in real time, they often turn to what many in the industry have started calling live chat, which has become increasingly popular over the years.
After - Clean extractable definition
Live chat is a real-time messaging tool embedded on a website that connects visitors directly with support agents or AI assistants. Unlike email or phone support, live chat enables sub-minute response times and supports multiple simultaneous conversations.
What This Actually Measures
We're detecting sentence structures that AI systems can extract as direct-answer definitions. When someone asks ChatGPT "What is AEO?" or "Define schema markup," the AI scans retrieved documents for recognized definition patterns -sentence forms presenting a term and its meaning in a single, extractable unit.
Six categories of patterns get detected. The "is-a" pattern: "AEO is the practice of optimizing content for AI answer engines." The "refers-to" pattern: "Schema markup refers to structured data vocabulary that helps machines understand content." The "defined-as" pattern: "Content freshness is defined as the recency of machine-readable timestamps on a page." The "means" pattern: "Entity authority means establishing your business as a recognized, verifiable entity." The "also-known-as" pattern: "Server-side rendering, also known as SSR, generates HTML on the server before sending it to the browser." The "parenthetical" pattern: "The canonical URL (the single authoritative version of a page) prevents duplicate content issues."
Two metrics per page: definition count (how many patterns appear) and definition prominence (do they appear within the first 200 words, where AI systems weight them most heavily?). The site-wide metric is the percentage of content pages with at least one clear definition in a prominent position.
This is distinct from the Tier 0 Q&A Content Format criterion. Q&A format checks whether headings use question structures. Definition pattern detection examines the sentence-level structure of the *answers* -specifically whether they use the grammatical forms AI extraction systems are optimized to parse.
Why Definitions Win the "What Is" War
"What is..." queries constitute roughly 14% of all queries directed at AI answer engines. These have a specific format expectation: concise definition, then elaboration. AI systems satisfy this by extracting definition-pattern sentences from retrieved sources and presenting them as the opening of their response.
When your content defines terms using clean patterns, AI systems extract and cite those definitions directly. When your content explains concepts through narrative, examples, or analogies *without* using definition patterns, the AI synthesizes its own definition. Synthesized definitions are less likely to cite your page because the AI is paraphrasing, not quoting.
At the site level, a domain consistently providing clean definition patterns builds a reputation as a definitional authority. AI systems learn that this domain reliably answers "What is..." queries with extractable definitions and start routing more definitional queries to it. This compounds -the more definitions you provide, the more your domain is trusted for definitions.
The competitive implication is direct. Your competitor's page defines "live chat software" with a clean is-a pattern in the first paragraph. Your page discusses live chat through examples and use cases without ever providing a definition sentence. The competitor gets cited for "What is live chat software?" regardless of which page has more depth. We've seen this play out across the live chat vertical (Tidio: 63, LiveChat: 59, Crisp: 34) -the sites with stronger definition patterns consistently win definitional queries.
How We Check This
Body text gets extracted from each content page and run through sentence-level NLP analysis. Three stages: sentence segmentation, pattern matching, quality scoring.
In segmentation, extracted text is split into individual sentences. Sentences shorter than 10 words or longer than 80 words are excluded -definitions under 10 words are too vague, and sentences over 80 words won't serve as extractable definitions even if they contain a pattern.
In pattern matching, each sentence is tested against the six definition categories using regex patterns and syntactic structure analysis. The patterns are tuned to avoid false positives -"The weather is nice" matches the literal "X is Y" pattern but isn't a definition. We apply semantic filters to exclude copular sentences describing states, opinions, or conditions rather than defining terms. The subject needs to be a noun phrase of substantive length (typically 2+ words or a recognized domain term).
In quality scoring, each detected definition is rated on specificity (enough detail to be useful?), completeness (sufficient for someone unfamiliar with the term?), and prominence (where on the page does it appear?). Definitions in the first paragraph or immediately following an H2 heading score higher than definitions buried mid-paragraph.
The output: a per-page report showing each detected definition with its pattern type, quality score, and position. The site-wide summary shows density distribution across content pages, most common pattern types, and percentage of pages with prominent definitions (first 200 words or directly under a heading).
How We Score It
Definition scoring combines coverage, quality, and prominence:
1. Definition coverage (4 points): - 70%+ of content pages contain at least one definition pattern: 4/4 points - 50-69%: 3/4 points - 30-49%: 2/4 points - 15-29%: 1/4 points - Below 15%: 0/4 points
2. Definition prominence (3 points): - 60%+ of pages with definitions have at least one in the first 200 words or under a heading: 3/3 points - 40-59%: 2/3 points - 20-39%: 1/3 points - Below 20% or definitions buried in middle paragraphs: 0/3 points
3. Definition quality and variety (3 points): - Average quality above 7/10, uses at least 3 different pattern types: 3/3 points - Average quality 5-7, uses 2+ types: 2/3 points - Average quality 3-5, predominantly one type: 1/3 points - Low quality or highly repetitive structure: 0/3 points
Bonus: - +0.5 points if definitions are paired with DefinedTerm schema markup
Deductions: - -1 point if more than 30% of definitions are duplicated across multiple pages (copy-paste definitions) - -0.5 points if definitions contain subjective language ("the best way to," "the most important") rather than factual descriptions
Most sites score 2-5. Technical documentation sites and glossary-rich knowledge bases score 7-10. Marketing-focused sites with primarily persuasive content score 0-3.
Resources
Key Takeaways
- Open key sections with a clean "[Term] is [definition]" sentence that AI can extract directly.
- Place definitions in the first 200 words or directly under an H2 heading for maximum prominence.
- Use varied patterns - "is," "refers to," "defined as" - to cover different extraction models.
- Avoid burying definitions inside narrative or examples - lead with the definition, then elaborate.
How does your site score on this criterion?
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