Do schema, llms.txt and Reddit get you cited by AI? What the evidence says

Three tactics, one test: what engines cite

Schema markup, an llms.txt file and a presence on Reddit are the three tactics most often sold as ways to get cited by ChatGPT, Gemini and Google's AI features. The evidence for each is thinner than the pitch. Schema did not move citations on pages that were already cited, published llms.txt files are rarely even requested, and company sites, not forums, draw most citations overall.

The test we apply is narrow on purpose. A tactic earns sprint time only if there is evidence it changes what engines cite, or if it is so cheap that the question hardly matters. Adoption rates, crawler hits and ranking signals are interesting. They are not citations.

Prior work, and our own interest

Others have covered each myth on its own. Ahrefs wrote up its schema test in detail¹, and Search Engine Journal reported Ahrefs' llms.txt data². This piece sets the three tactics side by side against the primary studies and Google's own documentation, then says what we still ship and why.

A disclosure first. Almost every study below comes from a company that sells SEO or AI-visibility tools or services. So does Sigzen AI: we sell AI visibility audits and sprints, and the sprint includes schema and llms.txt work. Weigh our view with that in mind.

Does schema markup get you cited?

Not measurably, on pages that engines already cite. That is what Ahrefs' controlled test found, and it is the most direct evidence on the question that we have read. Schema still has jobs to do. Getting an already visible page cited more often is not one of them, on this evidence.

What the controlled test found

Ahrefs, which sells SEO and AI-visibility tools, tracked 1,885 pages that were already heavily cited and then added JSON-LD schema between August 2025 and March 2026, against 4,000 matched controls¹. Citations moved +2.2% on ChatGPT and +2.4% on Google AI Mode, and neither change was statistically significant. On Google AI Overviews they fell 4.6%, a small decline that was significant¹.

Read the sample carefully. These pages were already cited, so the test says little about pages AI does not see at all. Ahrefs says so itself: "for pages that aren't being seen by AI systems at all, schema markup might still play a role."¹ Our view: that caveat is fair, but it is a hypothesis, not a result, and it should not be sold as one.

Why most cited pages carry schema anyway

In the same Ahrefs study (August 2025–March 2026), 53% of AI-cited pages carried schema, and Ahrefs reads that as correlation, not cause⁵. Both findings hold at once. Sites that bother with structured data tend to be the sites that also publish clear pages, keep facts current and earn links. Those habits get pages cited, and the markup comes along with them.

This is why an audit that flags "missing schema" as an AI-visibility problem can mislead. It sees the correlation and presents it as a lever. Our view: when a report shows you how many cited pages carry schema, ask for the controlled result next to it. Without one, the figure describes the pages that win. It does not explain why they win.

Where structured data still earns its place

Structured data still does useful work. It is simply different work from earning citations, and three jobs justify it.

• Product feeds and merchant data. Shopping answers draw on structured price, availability and variant data. A feed is a separate mechanism from page schema, and for retailers it often matters more. Our e-commerce and D2C page starts with the feed and the Product and FAQ schema for that reason, and our playbook for e-commerce brands sets out the feed checks.
• Rich results in classic search, which still send clicks to the pages that earn them.
• Entity consistency: the same name, description, founders and contact details on your site, your profiles and directories, so engines do not confuse you with someone else. Google asks that structured data match the visible text on the page⁶. If an engine already has your facts wrong, our guide to fixing wrong AI answers starts at the source page.

None of these jobs needs a promise about AI citations to justify it.

Does an llms.txt file help?

There is no evidence that it gets you cited, and Google says its AI features do not need one. The file is cheap, so keeping one is harmless. Paying for one as a citation tactic is not a good use of money.

What Google says

Google's documentation for site owners is unusually direct here. Its page "AI features and your website", last updated in December 2025, says: "You don't need to create new machine readable files, AI text files, or markup to appear in these features."⁶ It adds: "There's also no special schema.org structured data that you need to add."⁶

That statement covers AI Overviews and AI Mode only. It says nothing about ChatGPT or Perplexity. What Google asks for instead is ordinary: pages its crawler can reach, important content available as text, and structured data that matches what readers see. The llms.txt proposal is a convention some sites follow. No engine has told site owners that it cites pages because of it.

What the studies found

Of 137,210 sites in Ahrefs Web Analytics with traffic in May 2026, 28% published an llms.txt file, and 97% of those files received no requests that month³. Ahrefs adds that its customers skew technical, so the adoption figure is an upper bound³.

Note what this measures: requests to the file, not citations. A file nobody fetches cannot shape an answer. A fetched file would not prove influence either.

The citation question has been tested too. SE Ranking, in a study of nearly 300,000 domains published in November 2025 (data period not stated), found no link between having the file and being cited⁷. Our view: keep the file if you have one, because it takes minutes to update. Do not budget for it, and do not let anyone sell it to you as a citation lever.

Is Reddit what AI cites most?

Not overall. Across engines, company websites draw most citations. Reddit's strong showing was specific to ChatGPT at a particular time, and that has shifted too. The larger lesson is that source mixes differ by engine and move from month to month.

Company sites draw most citations overall

Profound, which sells an AI-visibility platform, counted 11.84 billion citations on eight engines in all 8,061 categories it tracks, from 16 April to 16 July 2026. About 57% pointed to company-owned websites: 47% on ChatGPT and 69% on Gemini⁴.

"Company-owned" means any company's site: corporate pages, product pages, brand blogs and documentation. It does not mean your site. A competitor's comparison page counts. So does a software vendor's help centre that happens to answer your buyer's question.

Our view: this is the strongest single argument against Reddit-first playbooks. If more than half of what engines cite is first-party company content, your own pages are the first place to compete, and the competitor pages being cited instead are the ones to study.

In late 2025, Reddit and Wikipedia led ChatGPT's citations

The Reddit story was true for one engine for a while. In Semrush's weekly snapshots from 14 July to 12 October 2025, Reddit and Wikipedia were ChatGPT's two most-cited domains⁸. That data is now more than a year old, and it described ChatGPT, not AI search as a whole.

Many "Reddit for GEO" playbooks were written in that window. Then the picture moved. Otterly, which sells AI-search monitoring, reported in August 2026 that ChatGPT sharply cut its Reddit citations and shifted toward reference and official pages⁹. We have not verified Otterly's figures, and it is a single study. It still makes the point: what one engine cited last year is a weak guide to what it cites now.

Engines differ, and they change

Engines also cite at very different volumes. In Semrush's 2026 AI Visibility Index, covering 126 million US prompts from January to April 2026, ChatGPT cited an average of 15 sources per response and Gemini an average of 3¹⁰. That is US data, and we would not assume it holds elsewhere.

An engine that cites many sources per answer leaves room for forums, reviews and trade press next to company pages. One that cites a handful tends to pick a few authoritative pages and stop. So "should we be on Reddit?" has no single answer. It depends on which engines your buyers use and what those engines cite for your prompts.

Our view: decide per engine, from your own prompt set, not from an average across engines.

What the evidence supports instead

Three things have better support: being cited at all, pages written so an engine can lift a passage, and mentions of your brand on sites you do not own. None of them is a shortcut, and each takes longer than adding markup.

Being cited is what pays

Seer Interactive, a search agency, tracked a panel of 53 brands from January 2025 to February 2026. Pages cited in a Google AI Overview earned 120% more organic clicks per impression than when they were not cited¹¹. The panel is Seer's own, so read it as a strong signal rather than a market average.

That is a Google result, and it measures clicks, not revenue. It still answers the question behind every tactic in this piece. Citation is the outcome worth paying for, and work that does not move it is overhead. If you do not yet know whether engines cite you at all, a free AI Visibility Score shows where you stand before you change anything.

Write pages an engine can quote

Pages that state a clear answer, cite their sources and carry specific facts give an engine something to lift. In a 2024 lab study, Aggarwal and colleagues found that adding citations, quotations and statistics to source pages were the best-performing methods, with relative gains of 30–40% on one of their visibility metrics¹². Keyword stuffing did little.

That benchmark ran on engines of its time, so treat the size of the gain as dated and the direction as useful. In practice it means an answer in the first lines of each section, a named source for every figure, and sections short enough to quote whole. This post follows the same pattern for the same reason.

Earn mentions beyond your own site

Your own pages are only part of the job. In May 2025, Ahrefs found that branded web mentions across 75,000 brands correlated more strongly with AI Overview visibility (0.664) than backlinks did (0.218)¹³. That is a correlation from a dated study, not proof that mentions cause citations.

It fits the rest of the evidence, though. If engines cite company pages for much of each answer, the remainder comes from reviews, trade press, directories and communities. Being described accurately in those places is slower work than markup. It is also where a sprint's effort pays back. Start with the domains engines already cite for your prompts, because those are the pages most likely to be read.

What we still ship on our own site, and why

We publish an llms.txt file and schema on sigzenai.com, and we will keep doing so. Both take minutes to maintain. The schema keeps our organisation and service details consistent for any system that reads them, and the llms.txt file is a plain map of our pages for any tool that asks for one. We do not expect either to get us cited.

Our AEO/GEO sprint (six weeks, from $8,000 (₹3.5L), our published price) lists the same work as "AI-crawler access, schema and llms.txt hygiene". It is there because blocked crawlers and broken markup are worth fixing, not because we promise citations from them. The sprint's effort goes into answer-first rewrites of your top pages, entity consistency and the first ten third-party citations.

What we sell in place of a promise is measurement: a prompt set run before and after, so each tactic is kept or dropped on evidence. Our playbooks describe the checks we run. To see what other providers put in a sprint, and at what price, read our dated list of published GEO prices.

Where to spend your next sprint

Spend it on pages and mentions, in that order, and keep the hygiene items to an afternoon.

1. Fix access first. Check that robots rules let search and AI crawlers in, and that key copy is in the HTML your server sends, not added later by scripts.
2. Rewrite the pages your buyers' prompts should land on, so each section opens with its answer and names its sources.
3. Find which outside domains engines cite for your prompts, then earn accurate mentions there: reviews, directories, trade press, and communities where your buyers actually talk.
4. Keep schema accurate and matched to visible text, and keep llms.txt current if you have one.

How the tactics compare

Plotting the common tactics by effort and by strength of evidence makes the trade-off plain. The cheap ones are worth doing for reasons other than citations. The ones with better support cost real time.

Our view: if a proposal leads with schema or llms.txt as its citation strategy, ask what evidence it rests on, and for which engines.

How do you test a tactic on your own prompts?

Run a fixed prompt set before and after the change, several times each, on every engine that matters to you. Then compare the share of answers that name or cite you, and change one thing at a time.

AI answers vary from run to run, so a single before-and-after check proves little. You need repeated runs and a margin of error on each number. Our measurement spec, Can you trust your AI visibility score?, sets out what to ask any vendor for, including us.

If the change is schema or llms.txt, expect the honest result to be no detectable difference. That is still worth knowing, because it tells you where not to spend the next sprint.