How to Get Cited for What Is and Definitional Queries

To get cited for "what is" and definitional queries, lead with a single, quotable sentence that answers the question in under 25 words, back it with one sourced statistic, mark it up with Article and DefinedTerm schema, and track citation rate by engine so you know what to fix first.

ChatGPT now handles more than 900 million weekly active users, and a large share of what people type into it starts with "what is," "what does X mean," or "define." OpenAI confirmed the 900 million figure in February 2026, roughly double the 400 million it reported a year earlier (TechCrunch). Every one of those definitional prompts is a moment where an AI engine picks exactly one explanation to surface, sometimes with a citation, sometimes without one. If your content is not written and structured to be that one answer, you are invisible for the query, no matter how well you rank in classic search.

The stakes for getting this wrong keep climbing. Google's AI Overviews now cut click-through rate for the position-1 organic result by 58%, up from 34.5% just eight months earlier, and zero-click sessions have risen from roughly 54% to 72% of all searches over the same window (Ahrefs; Search Engine Land). Definitional and informational queries, the "what is" and "how does X work" category, are consistently among the search types most likely to trigger an AI-generated answer instead of a list of blue links. That makes this one query category worth a disproportionate share of your GEO effort.

This guide breaks down how AI engines actually answer definitional queries, what a citeable definition looks like on the page, how to structure a glossary or definitional content hub, and how to measure whether it is working.

Why Definitional Queries Are the New Front Line

"What is" queries used to be some of the least valuable real estate in SEO. Low commercial intent, thin content, a snippet at the top, nobody fought hard for them. That has flipped.

AI engines treat definitional queries as their easiest win. The question has one correct-ish answer, source material is abundant, and a confident, well-cited response builds user trust in the assistant itself. That means definitional prompts get answered directly inside ChatGPT, Gemini, Claude, Perplexity, and Google's AI Overviews far more often than transactional or navigational queries do, and the citation attached to that answer is now one of the few places your brand name and URL still surface in front of the user.

Three things make this category different from the rest of your GEO strategy:

• Volume is enormous and evergreen. Definitional questions do not decay the way news or product queries do. A strong definition of a term in your category can earn citations for years with light maintenance.
• The bar for "correct" is lower, the bar for "quotable" is higher. Engines are not judging your definition against a rubric of comprehensiveness. They are looking for the passage that is easiest to lift cleanly into a response.
• Competition is often weaker than it looks. Definitional content is unglamorous, so many category leaders never invest in it properly, leaving room for a sharper, better-structured answer to win the citation even from a smaller domain.

How AI Engines Actually Answer "What Is" Questions

Every major AI answer engine, whether it is Google's AI Overviews, ChatGPT, Perplexity, or Claude, runs a version of the same four-stage pipeline when it handles a definitional query.

1. Classify. The system decides whether this prompt should get a direct synthesized answer versus a list of links or a clarifying question. Clear "what is X" phrasing almost always triggers direct synthesis.
2. Select sources. The engine pulls a shortlist of candidate pages, weighing authority, freshness, structure, and, for some engines, real-time search results.
3. Extract passages. Instead of reading the full page, the model extracts the specific sentences or paragraphs that most directly answer the question.
4. Synthesize and cite. The model blends the extracted passages into one answer in its own words and, depending on the engine, attaches a citation link to the source it leaned on most.

The critical implication sits in step three. Your entire page does not need to be extractable, one paragraph does. If that paragraph is buried under three paragraphs of brand narrative, an extraction model may never reach it, or may pull a cleaner passage from a competitor instead.

Which sources get selected also varies more than most marketers assume. No single domain dominates across every model. Wikipedia is ChatGPT's single most-cited source, showing up in roughly 12% of its citations, while Claude and Perplexity lean on it far less (Analyze.ai). Reddit tells the opposite story: independent citation-pattern research found it is the leading cited source for both Google AI Overviews and Perplexity, even though its share inside ChatGPT is much smaller (Profound). A definitional page optimized around one engine's favorite sources can be nearly invisible in another. Understanding how AI engines cite brands differently is step one before you write a single definition.

*How ChatGPT, Gemini, Perplexity, and Google AI Overviews process a "what is" query before they decide who gets the citation.*

The Anatomy of a Citeable Definition

A citeable definition is a specific piece of writing, not a vibe. It has four components, in order, and each one does a different job.

The one-sentence, standalone definition

This is the sentence a model can lift verbatim, or near-verbatim, and drop into a response without losing meaning. It defines the term, states the category it belongs to, and stops. Aim for under 25 words. If your definition needs a subordinate clause to make sense out of context, it is not standalone yet.

One sourced statistic

Princeton's GEO research, presented at KDD 2024, found that adding relevant statistics to a passage improved its visibility in AI-generated answers by up to 41%, and that including direct quotations added another 28% (arXiv). For pages that started out ranked low, citing outside sources improved visibility by as much as 115%. A definition with a number attached is measurably more likely to be the passage an engine chooses to extract.

Context in plain language

One or two sentences explaining why the term matters or how it is used in practice. This is where you earn the "helpful" signal without diluting the standalone sentence above it.

A concrete example

A real scenario, a named tool, or a short comparison. Abstract definitions get paraphrased into something generic. Specific ones get quoted directly, because there is nothing generic left to paraphrase.

*The four-part structure of a definition built to be extracted and cited, not just read.*

Building a Definitional Content Framework

Individual definitions perform better when they live inside a system rather than as one-off blog paragraphs.

Glossary pages

A dedicated glossary, structured as one term per URL or one term per clearly anchored section, gives AI crawlers an unambiguous, single-purpose page to extract from. Promptive's own glossary follows this pattern: term, standalone definition, context, and a link to deeper coverage. Each entry is short enough to read in full but complete enough to stand alone as a citation.

Content hubs

Glossary entries work best when they connect to a broader hub of related, deeper content rather than sitting in isolation. A well-linked hub signals topical authority to classic crawlers and AI retrieval systems alike, and it gives readers somewhere to go once the definition has answered their immediate question. If you have not already, building a content hub AI engines cite as a source is the natural next step once your glossary is live.

One term, one page

Resist the urge to cover five related terms in a single glossary entry to save time. Engines extract passages, not pages, but a page that visibly tries to cover too much ground signals lower confidence to a retrieval system scoring relevance, and it splits your internal linking equity across concepts that should each earn their own citations.

Technical Setup: Schema, Structure, and Crawlability

Schema markup does not directly cause an AI engine to cite you, but it does make your content dramatically easier for a model to parse correctly, which matters at the extraction stage described above.

Two schema types marketers still lean on lost their SERP rich-result treatment: HowTo rich results disappeared from Google back in 2023, and FAQ rich results were removed on May 7, 2026. Neither loss means the schema itself stopped mattering. Structured data is still a clean, explicit signal for machine parsers, it just no longer buys you a visual upgrade in classic search results.

Beyond schema, three technical basics decide whether any of this reaches a model at all: the page must render without depending on client-side JavaScript for major AI crawlers, it must load fast enough not to get truncated by a crawl budget cutoff, and it must be reachable from internal links, not orphaned three clicks deep. Our guide on using schema markup to win AI citations covers implementation in more depth, and our piece on the sources AI engines cite most explains which domain-level signals compound on top of schema.

The Step-by-Step Process

Use this sequence for every new definitional page or glossary entry:

1. Pull your prompt list for "what is," "what does X mean," and "define" phrasing tied to your category.
2. Draft the one-sentence, standalone definition first, before any other copy.
3. Attach one sourced statistic and one concrete, named example.
4. Add one or two sentences of plain-language context underneath.
5. Structure the full page: definition, context, comparison or example, then FAQ.
6. Mark up the page with Article/BlogPosting schema, plus DefinedTerm or FAQPage where relevant.
7. Publish on a fast, crawlable URL with clear internal links from your hub and navigation.
8. Submit the URL through Google Search Console and request indexing.
9. Monitor citation rate by engine and by prompt, and revise whichever definitions are losing to a competitor's.

Common Mistakes That Keep Brands Out of AI Answers

• Burying the definition. Three paragraphs of brand story before the actual answer means most extraction systems never reach the sentence that matters.
• Writing technically correct but unquotable definitions. Jargon-heavy, run-on sentences do not survive extraction cleanly, so a model paraphrases you into something generic, or skips you for a competitor.
• Skipping the statistic. A definition with no number attached is measurably less likely to be the passage an engine selects, per the Princeton GEO findings above.
• Covering five terms on one page. It dilutes relevance signals and makes none of the definitions a clean, standalone extraction target.
• Schema that does not match visible text. Structured data describing content that is not actually visible on the page reads as a trust signal in reverse.
• Optimizing for one engine only. ChatGPT, Perplexity, and Google AI Overviews each lean on different top sources, so a single-engine strategy leaves most of the market uncovered.
• Publishing once and walking away. Definitional citations are contestable. A competitor can out-structure you next quarter if nobody is watching.

Measuring and Iterating

You cannot fix what you cannot see, and citation for definitional queries is invisible in standard analytics. A ChatGPT or AI Overview citation does not show up as a keyword ranking, and it may not even show up as a referral click.

The practical fix is to track citation rate directly: for a defined list of "what is" and definitional prompts in your category, how often does each AI engine cite your domain versus a competitor's, and how does that sentiment and share of voice shift month over month. Running a free GEO audit is the fastest way to get a baseline before you touch a single page, so you know which definitional queries you are already winning and which ones are worth the rewrite first.

Once you have a baseline, treat definitional pages the way you would treat a page-one keyword ranking: check monthly, not annually. Engines update their retrieval indexes continuously, and a competitor's better-structured glossary entry can displace you within weeks, not the months or years it used to take to lose an organic ranking.

Frequently Asked Questions

What counts as a "definitional" or "what is" query in GEO?

Any prompt where the user is asking for the meaning, category, or basic explanation of a term, concept, or acronym, phrased as "what is," "what does X mean," "define X," or "X vs Y, what's the difference." These queries trigger direct AI answers more consistently than transactional searches.

How long should a definition be to get cited by AI engines?

The core sentence should be under 25 words and stand alone without needing the rest of the paragraph for context. The full entry, including context and an example, typically runs 100 to 200 words, short enough to extract cleanly and long enough to be genuinely useful.

Do FAQPage and HowTo schema still matter if their rich results are gone?

Yes. Losing the SERP rich-result treatment only removed the visual upgrade in classic search results, not the underlying value. The structured data itself remains a clean, explicit signal that helps AI crawlers parse Q&A pairs and step sequences correctly during the extraction stage of a response.

Which AI engine cites definitional content most often?

There is no single answer. Wikipedia's share concentrates heavily inside ChatGPT specifically, at roughly 12% of its citations, while Reddit leads citation share for Google AI Overviews and Perplexity instead, so engine-specific testing matters more than picking one favorite.

How is citation for definitional queries different from ranking for a keyword?

Keyword rankings are relatively stable and visible in tools like Search Console. AI citations are extracted per response, can change week to week as models re-crawl, and rarely show up in standard analytics, which is why dedicated citation tracking matters.

Should every definition live on its own page or inside a content hub?

Both, and they work together. Give each term its own clean, standalone entry so it is a precise extraction target, then link it into a broader hub of related content so it also signals topical authority and gives readers somewhere to go next.

How often should I update definitional content?

Review your highest-value definitional pages monthly against what competitors are getting cited for instead of you. Retrieval indexes refresh continuously, and models re-rank source passages often, so a stale or poorly structured definition can lose its citation to a sharper competitor within weeks, not months.

Can a brand-new domain get cited for a "what is" query?

Yes, more easily than for competitive commercial queries. Definitional content rewards structure and clarity over raw domain authority, so a new site with a sharper, better-sourced, well-marked-up definition can out-cite an established competitor that buried its answer under paragraphs of unrelated brand narrative.