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What Makes ChatGPT Cite Your Pages, Updated October 2026

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Last week I was doing my usual doomscroll through a few thousand LinkedIn posts about SEO, because that is genuinely most of what my feed is, and a study stopped the scroll. A SISTRIX analysis of 96,418 URLs tested which page features come with higher citation odds in AI search, and it read like a checklist of what I have been building for over a decade: tables of contents, article schema, real H2 rhythms, visible update dates.

Two findings stopped me cold, though. The current year in the title carried a 2.7x citation multiplier, and FAQ schema, which SEOs spent much of 2025 telling each other to stop worrying about, measured 1.5x. The study also found no measurable effect from llms.txt, which tracks with my own choice never to install it on client sites.

I am getting more interested in showing up in LLMs than in doing regular SEO, and this study is the kind of evidence that decides where the effort goes. So let me walk through it.

What SISTRIX studied and why the comparison matters

SISTRIX analyzed 48,527 URLs cited by ChatGPT, Google AI Mode, and Google AI Overviews in the DACH region during Q2 2026. It paired each cited page with a non-cited page from the same domain, then analyzed 96,418 URLs across 17,380 domains using 43 page-level features and conditional logistic regression.

That paired design is useful. Comparing pages on the same domain helps control for site-level authority and brand strength. The study is asking what cited pages have in common with uncited siblings on the same site.

The reported multipliers describe associations, not causes. A page with a table of contents was cited 1.7 times as often as its same-domain counterpart without one. That does not prove adding a table of contents will produce more citations on any individual site.

The research was a DACH-region snapshot and did not include every AI platform. I first saw it alongside other recent findings in this weekly AI search roundup from PromptMarketing.

Which page features were associated with more citations

SISTRIX reported these citation multipliers:

  • Table of contents: 1.7x
  • Article schema: 1.6x
  • Five or more H2 headings: 1.6x
  • FAQPage schema: 1.5x
  • Table with a header row: 1.5x
  • A question used as an H2: 1.4x
  • Visible last-updated date: 1.3x
  • Current year in the title: 2.7x

An older year alone in the title was associated with a 0.7 multiplier. The current-year result is an early signal: SISTRIX added title-year features retrospectively, so it recommends treating them as initial indications.

Most of that list is stuff I have shipped on client sites for years without needing a study to justify it. Headings on a rhythm, schema that matches the content, dates that show the page is alive. The one I had not been deliberate about is the year in the title, and I am starting now. If a 2.7x association comes from a feature that costs one word to add, that is the cheapest test on the list.

Only 14% of pages had a table of contents. Just 5% showed a visible last-updated date. Pages combining many of the studied features were cited 2.1 times as often, and only 10% of cited pages had that combination.

That is the part I find most useful. A single tactic rarely carries the whole job. A well-structured page gives an answer engine multiple clear ways to identify, extract, and attribute the information.

A few bars rise above the others in a clean cyan, mauve, and blush-pink chart

What showed no measurable effect

SISTRIX found no measurable citation effect for HowTo, QAPage, or breadcrumb schema. Each scored 1.0. Readability scored 1.03, which the study treated as no significant effect. Product schema scored 0.8 overall and 0.5 in AI Overviews.

Those results do not make readability or product schema pointless. They describe citation patterns in this dataset. Readability still matters to people, and Product schema can support other search and shopping uses.

The FAQPage one deserves its own paragraph, because the SEO conversation on this flip-flopped hard. Not long ago the consensus was to stop bothering with FAQ schema, and I watched that take travel through the same LinkedIn feed where I found this study. A 1.5x association on a sample this size says the consensus outran the data again, so I am keeping FAQ schema in the toolkit.

The llms.txt finding deserves its own line. Domains with an llms.txt file accounted for 11.7% of cited URLs, compared with 14.0% across all domains. SISTRIX found no measurable citation advantage. Since llms.txt applies at the domain level, the comparison also involves differences between domains, so the result does not establish that the file causes harm. It simply gives no evidence to prioritize it for citations.

I have spent enough time watching SEO recommendations travel faster than their supporting data. This is a useful reminder to ask what a tactic actually changes before adding it to a client checklist.

Put the answer where a system can find it

A separate RESONEO study of 76,450 shared Gemini conversations and 222,753 Google searches looked at the location of cited passages. It found that 33% of cited passages began in the first 10% of the text, and 80% began in the first half. Half the passages were 43 words or shorter.

The practical takeaway is straightforward. Put the direct answer near the top, then support it with useful details. Write short passages that can stand on their own without stripping away the context that makes them accurate.

This matches the way I build content. I answer the question early, use headings on a steady rhythm, and add tables when they make real information easier to scan. A table earns its space when it organizes useful facts, not when it exists to tick a box.

A clean document page with a contents list, section blocks, and a table represented by blank cells

Rankings are an incomplete proxy for AI citations

In a High Salience study of ChatGPT sources, 79% of the pages ChatGPT cited, 175 of 221, appeared on neither Google’s nor Bing’s first page for the searches examined. Small sites made up 26% of pages ChatGPT pulled up and 14% of pages it cited.

That finding does not mean rankings have stopped mattering. It means a traditional ranking report cannot describe all AI citation visibility. A page can be cited beyond the first page, and smaller sites appeared among both the pages ChatGPT retrieved and the sources it used.

For smaller sites, that creates room to compete through strong, clear pages. Structure gives an answer engine a page that is easier to interpret and quote. No study can promise a citation, but improving the page itself is work an operator can control.

The sample-size lesson is worth stealing

Here is my favorite part, and it is the nerdiest. SISTRIX tested FAQ schema at three sample sizes and got contradictory results. At 833 URLs, FAQ schema was 23% less common on cited pages. At 4,600 URLs, it was 31% more common. At 96,418 URLs, it was 19% more common.

Its conclusion: be cautious with GEO studies that analyze fewer than 5,000 URLs.

That methodological warning matches my own skepticism about small-sample studies. A few dozen prompts or a small collection of URLs can point toward a question worth investigating. It takes a much larger sample to make a strong claim about what works broadly.

When the evidence was thin, I said so in my own posts instead of pretending I had a slam dunk. Turning a tiny test into a universal rule is how SEO ends up with another myth. If you read the methodology section before the headline, demand a real sample size before you believe a study.

How I use these findings in my content work

I have worked in SEO since 2013 and learned through hands-on client work. This study reads like a checklist of practices I already use: clear headings, answer-first openings, tables with meaningful data, and visible freshness signals. My freshness-stamp rule predates this research.

When I include Ahrefs or Semrush data, I label the source. I want readers and systems to see where a number came from, and I want the table to help someone understand the result. My approach to SEO content strategy and content SEO strategy for AI search starts with the reader’s question and the evidence available to answer it.

I also track citations and publish an AI visibility checker. My broader work in AI search optimization connects page improvements with visibility monitoring. On agency projects, Evergreen Digital owns the work. Founder Brady Madden sets strategic direction on most accounts, and I build strategy myself on some of them.

Start with the pages buyers need

Pick a page that answers an important customer question. Check that the answer appears early, headings make the page easy to navigate, and tables have clear header rows. Add schema that fits the content, then display an update date when the page has actually been reviewed and refreshed.

For the technical side, my technical SEO page covers the site foundations that help search systems access and understand content. If you want a practical place to begin, use my free SEO audit.

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