What Gets Cited in AI Answers? Query Type Beats Topic

“How do I get cited by AI?” usually gets answered with topic advice — write about your niche, build authority in your vertical. New data says that’s the wrong first question. The bigger lever isn’t what you write about. It’s the shape of the question your content answers.

cloro’s AI Search Index ran the same frozen basket of 735 queries through five engines — ChatGPT, Perplexity, Copilot, Gemini, and Google AI Mode — with an equal number of prompts in every combination of eight industries and six query types. Because the basket is balanced, it can separate two things a normal sample tangles together: does citation behavior depend on the topic, or on the kind of question? The answer is lopsided.

Query type moves citations ~2× more than topic

Here’s how often an AI answer carried at least one citation, broken out by query archetype — pooled across all five engines, 120 prompts each, running from the most commercial intent (“best CRM for startups”) down to the most definitional (“what is a REST API?”):

That’s a 10.5-point spread from top to bottom, and it’s not random: the ranking runs cleanly from commercial intent down to definitional intent. “Best X” and buying-guidance queries — the ones where the answer hinges on current, specific facts like prices, rankings, and product specs — pull a citation nine times out of ten. Definitions, which an LLM can answer confidently from its own training, reach for an external source least often.

Now compare that to the same metric cut by topic. Every vertical gets the same 90 prompts, so this is a like-for-like read — and on the same scale, the bars barely move:

The entire eight-vertical range fits inside 5.4 points — and setting aside education (82.4%), the other seven sit within about two points of each other (85.6–87.8%). Whether you’re in travel or SaaS or health, an AI answer is about equally likely to cite someone. The variation lives in the query, not the subject.

Put the two side by side and the story is hard to miss: query type accounts for roughly twice the swing that topic does. Citation presence is intent-driven, not vertical-driven.

Why this reframes GEO

Most generative-engine-optimization advice is organized around topics and authority — become the trusted voice in your niche and the citations follow. That’s not wrong, but it’s the second question. The first is whether the queries you’re targeting are the kind that get cited at all.

A page built around a definitional head term (“what is X”) is swimming against a 10-point current before it starts: those answers cite a source only ~81% of the time, and when the model does cite, it’s choosier. A page built around a top commercial intent (“best X”, “which X should I buy”) is aimed at answers that cite ~90% of the time — comparison and how-to shapes land a notch lower (~83%), still comfortably above definitions. Same effort, meaningfully different odds of appearing in the citation list.

This maps directly onto content planning:

• Lead with high-citation intents. Ranked lists and buying guides sit at the top of the citation-presence chart (91% and 90%). If getting cited is the goal, these commercial formats are the highest-yield place to spend.
• Don’t abandon definitions and how-tos — arm them. They’re cited less because the model can often answer them itself. To flip that, give it something it can’t generate: named data, dated figures, concrete and attributable claims. A definition that carries a specific, verifiable statistic is a better citation candidate than one that restates common knowledge.
• Stop over-indexing on your vertical. The data says your industry barely changes your citation odds. Effort spent worrying “is my niche too obscure to get cited?” is better spent on the query shapes above.

For the fuller framework — schema, structure, and formatting that make a page quotable — see our generative engine optimization guide and the GEO checklist.

The engine caveat: presence is only half the picture

Getting cited at all is query-driven. But how many sources an engine attaches, and which ones, varies enormously by engine — and that’s a separate lever.

In the same index, four of the five engines cite on 98–100% of answers while Gemini cites on just 33%; Google AI Mode backs an answer with ~20 sources where Copilot uses ~5. We broke the anatomy of that down across ~13,000 answers in how each AI engine cites sources, and mapped which engines ground their answers in live sources in AI grounding by engine.

There’s also a durable pattern in what gets cited: pool the sources across engines and the top of the list is user-generated platforms — YouTube and Reddit lead — not publisher homepages. That’s why Reddit shows up in AI citations far more than its traffic would suggest, and it’s another reason the query-shape question matters: the formats that win citations (lists, comparisons, first-hand experience) are exactly what those platforms are full of.

An independent analysis of roughly 8,000 AI citations by Search Engine Land shows the same divide from the source side: ChatGPT leans on authoritative references (Wikipedia was ~27% of its citations) and almost never cites forums, while Google’s AI surfaces pull heavily on Reddit and community content. It also found that query intent — B2B versus B2C — reshapes which sources an engine trusts, echoing what the balanced basket shows about query shape.

What to do with this

Three moves, in order:

1. Audit your target queries by intent, not just volume. Sort your priority prompts into the six archetypes above. The ones sitting in “best-in-class”, “buying guidance”, and “comparison” are your best citation bets; treat definitional and how-to targets as needing extra attributable substance.

2. Earn the citation once you’ve picked the intent. Presence is a population average — sitting in a high-citation query is the ante, not the win. Four levers move you from “an answer here gets cited” to “this answer cites you”:

   • Match the format to the intent. Commercial intents get cited as ranked lists, comparison tables, and buying guides — so publish that shape, not a wall of prose. It’s the format the engine is already reaching for.
   • Write for extraction. Engines lift self-contained “chunks,” so make claims that stand on their own: named, dated, specific. Semrush’s guide to AI citations lands on the same rule — write sentences that make sense out of context so an LLM can quote them cleanly.
   • Back it with authority. Original data and third-party mentions are what tip a close call. That same Search Engine Land analysis found foundational search authority precedes AI citations rather than following them — AI visibility is an outcome of broad web credibility, not a separate trick.
   • Stay technically citable. None of it matters if a crawler can’t parse the page: prefer server-rendered HTML, add schema markup (Google recommends JSON-LD), and publish an llms.txt so engines can reach and read your content.

3. Measure whether it’s working. Run your priority prompts across every engine on a schedule and track whether your domain lands in the citation list, per engine, over time. That’s exactly the pipeline cloro’s AI visibility tracking is built for, and the same data that produced the AI Search Index behind this post.

The headline is simple enough to act on today: before you ask “what should I write about,” ask “what kind of question does this answer” — because on the current data, that’s the bigger lever on whether AI cites you.

All figures in this post are from cloro’s AI Search Index (Edition 01, July 2026), a recurring measurement of AI citation behavior on a fixed, balanced 735-prompt basket. See the study for full methodology.