If an AI assistant can find your page but can't lift a clean answer out of it, you won't get cited, no matter how authoritative the page is. Crawl access and topical depth are necessary conditions for citation, not sufficient ones: once a page is indexed and sits on a domain that covers its topic seriously, the deciding factor is often just how that specific page is written. That's a narrower, more mechanical problem than "build topical authority" or "get crawled," and it's one you can fix on a single page in an afternoon. This post covers that layer on its own: the structural and formatting choices that make a page easy for a retrieval system to pull a passage from, and easy for a language model to quote accurately once it does.
Why formatting is a separate lever from crawl access or topical depth
Crawl access determines whether an AI system can see a page at all. Topical depth determines whether a domain looks credible enough on a subject to be worth citing from. Neither has much to do with the sentence-level and structural choices made on the page a language model is actually trying to quote, and that's the layer most sites still write on autopilot.
Researchers at Princeton, Georgia Tech, the Allen Institute for AI, and IIT Delhi tested this directly with a framework they call GEO, for Generative Engine Optimization: black-box, content-level interventions, adding statistics, adding quotations, citing sources, tightening structure, run against a benchmark of real user queries and measured against how often generative search systems surfaced and quoted the resulting content. The best-performing interventions improved a page's visibility in generative engine responses by up to 40% (Aggarwal et al., KDD 2024). Citing sources and adding statistics were consistently among the strongest single levers, and the gains compounded when combined. None of that required the underlying domain to be more authoritative or the topic to be covered more broadly. It required rewriting the page.
1. Lead every section with a direct, extractable answer
The single highest-leverage formatting habit is the inverted pyramid: the newspaper convention of putting the most important information first and layering in supporting detail afterward. This isn't a new invention for the AI era, it's standard scannable-writing practice, and the reason it still works is data, not folklore. In Nielsen Norman Group's original eye-tracking research on how people read on the web, the large majority of participants scanned rather than read start to finish, absorbing well under a third of the words on an average page (NN/g). A retrieval system pulling a passage out of your page to answer a question is, functionally, doing an aggressive version of the same scan: it isn't reading your page, it's hunting for the two or three sentences that answer the question in front of it.
Practically: every H2 and H3 should open with a two-to-three sentence answer to the question implied by that heading, before any throat-clearing, scene-setting, or "in this section we'll cover." If a reader, or a model, stopped after your first sentence under a heading, they should already have the answer, even if not the full nuance.
2. Phrase headers the way people actually ask AI assistants questions
Headings do double duty now: they're a scanning aid for humans and a matching signal for retrieval systems trying to align a user's question with a section of your page. A heading like "The New Frontier of Content Discovery" carries almost no matchable signal. A heading like "How long should a blog post paragraph be?" matches, almost verbatim, the kind of question someone actually types into ChatGPT or asks Claude.
This doesn't mean every heading needs a question mark. It means the heading should describe, in plain language, the specific thing answered underneath it; question-phrasing is often just the most natural way to get there. Save cute, branded, or metaphor-driven headings for content where the goal is persuasion rather than extraction.
3. Write claim sentences that stand alone
A quote pulled by a language model rarely arrives with the paragraph around it intact. That means every sentence carrying a specific claim, a statistic, a definition, a recommendation, needs to make sense on its own: no pronoun pointing back three sentences, no "as mentioned above," no claim split across two sentences joined by "however."
Compare:
- Weak: "This matters a lot. It was found to make a big difference in a recent study, more than most people expect."
- Better: "Adding statistics and citations to a page's content improved its visibility in AI-generated answers by up to 40% in a 2024 Princeton-led study."
The second version survives being lifted out of context because it already carries its own context: what changed, by how much, and where the number comes from.
4. Use lists and tables only when the data is genuinely comparable
Bulleted lists and tables are good at one specific job: presenting items that share the same attributes, so a reader, or a model, can compare them at a glance, crawler names and their purposes, pricing tiers, before-and-after numbers. They're a poor substitute for prose when the content doesn't actually have that shape. Chopping a paragraph into fragments doesn't make it more structured, it just makes the underlying claim harder to quote as a coherent sentence.
A useful test: if you can't write a one-sentence header describing what every row or bullet has in common, the content isn't list-shaped yet, and forcing it into a list will bury the answer instead of surfacing it.
5. Know what schema markup can and can't do for you
Structured data, JSON-LD implementing schema.org types like Article, FAQPage, or HowTo, is often pitched as an AI-citation lever. Treat that claim carefully, because two of the three schema types most commonly recommended for this no longer do what people assume.
Google retired HowTo rich results from Search entirely in September 2023, and retired FAQ rich results in May 2026 (Google Search Central: FAQPage, HowTo). Neither markup produces a visual rich result in Google Search anymore, and no major AI search platform has documented FAQPage or HowTo schema as an input to its citation logic. If you're adding either one hoping for a citation lift, there is currently no evidence it does anything.
What schema still does reliably: it gives machines an unambiguous, structured description of what a page is, Article schema exposing a headline, author, and publish date in a consistent format a parser doesn't have to guess at (schema.org/Article; Google's general structured data guidelines). That's a modest, real benefit for parsing and attribution, not a citation hack. It's also exactly how this site's own posts are marked up: every post carries Article and breadcrumb schema automatically, and FAQ schema is added only to pages with a genuine, substantive FAQ section, never as a bolt-on for search credit it no longer earns.
Putting it together
None of the five practices above requires touching your hosting, your crawl budget, or your backlink profile. They're prose and heading decisions you can make on a single page in one editing pass: answer first, question-shaped headers, self-contained sentences, list only what's actually list-shaped, and schema for clarity rather than for a rich-result reward that no longer exists for two of the three types most commonly recommended.
Crawl access is a separate, largely one-time technical fix, and we've covered it in depth in our breakdown of llms.txt and whether it actually helps.
If you've made these changes and still aren't seeing citations, the more likely culprits sit upstream of formatting: crawl access, or whether your domain has covered the topic broadly enough to be treated as a credible source on it. For the complete picture, including platform-specific crawler requirements and how to measure whether any of this is working, see our full guide to getting your website into AI search results.
Tom Eastwood is the founder of Citation Hawk, a tool that monitors AI citation rates across ChatGPT, Claude, and Google AI Overviews.