How NOT to Get Cited by LLMs: 10 Well-Known GEO Tactics Debunked

Schema, llms.txt, word counts, the +40% GEO recipe: we checked the 2024–2026 studies. Here are the tactics that don't get you cited, and what does instead.

Dite Gashi

GTM Engineer, 15 years as a full-stack developer.

Published on October 31, 2026

Updated on November 3, 2026

12 min read2400 words

A student studying at a desk with notes and a laptop, illustrating study tips.

Popular GEO tactics promise AI citations, but the data shows most of what's on your page barely moves the needle.

Key Takeaways

Getting found by ChatGPT's own web search explains about a third of which pages it cites. Everything on the page itself, combined, explains under 3%.¹
Packing pages with stats and quotes mostly fails when it's tested again.³
Schema markup and llms.txt don't get you cited.^5,7
Page length, FAQ blocks and tables don't move citations. Titles that match the question and openings that answer it do.¹
AI-written pages aren't penalised. Mass-produced pages that add nothing new are.^15,16

Why most GEO advice doesn't hold up

AI answers cite what their own search finds, not what a checklist polishes.

Here's how it works. When you ask ChatGPT something that needs current information, it doesn't answer from memory. It runs a web search, reads a few results, and writes its answer from those. That search step is called retrieval.

If your page doesn't come back in that search, the model never sees it. No formatting trick can fix that.

A 90-day study of ChatGPT citations put numbers on it:¹

Showing up in the search explained about 32% of why some pages got cited and others didn't.
Everything on the page (length, formatting, schema, FAQ blocks and more) explained 2.8%, all together.

Most GEO guides optimize the 2.8% and ignore the 32%. A lot of that advice also comes from companies selling AI-visibility tools, so it's written to sound actionable more than to be right.

Below are ten popular tactics, what the evidence says about each, and what to do instead.

The short version: what to do this month

Rank first. AI search leans on regular search results. Your normal SEO work is your GEO work.
Write titles that match the question people actually type.
Answer in the first paragraph. Put the answer up front, then explain.
Add facts nobody else has: your prices, your numbers, your results, with real dates.
Get mentioned on other sites. Reviews, podcasts, roundups and forums count more than backlinks.
Make sure crawlers can read your pages without running JavaScript, and that you aren't blocking them.

Myth 1: Packing your article with stats, quotes and citations

Extra stats only helped once the page was already in front of the AI.

This advice traces back to one 2024 study, the paper that coined the term "GEO." The researchers took 10,000 questions, handed GPT-3.5 the top 5 Google results for each, then rewrote those pages in different ways to see which version the AI quoted more. Pages rewritten with added quotes, stats and citations showed up as much as 40% more in the answers.² That "+40%" is where most GEO checklists come from.

There are two catches. The AI was handed the pages directly, so the study never tested whether a page gets found in the first place. And the gains mostly came from taking credit from other pages: the 5th result went up 115% while the 1st result dropped 30%.²

Later re-tests were worse. One found only 3 wins out of 54 tries, and adding stats hurt in 19 of 24 cases.³ Another tested the full search-and-answer process and found these rewrites lowered visibility at every step.⁴

Do this instead: cite real sources because it makes your writing better, not as a trick. Never invent stats or quotes.

Myth 2: JSON-LD schema gets you cited

In the best controlled test, schema made no real difference.

Ahrefs compared 1,885 pages that added schema against similar pages that didn't. AI Overviews citations actually dropped 4.6%. ChatGPT and AI Mode barely moved.⁵ AI assistants read the visible text on your page and skip the schema. Google says so directly: there's "no special schema.org structured data that you need to add" for AI features.⁶

Do this instead: keep schema for Google rich results (stars, prices, FAQs in regular search). Don't expect it to get you into AI answers.

Myth 3: llms.txt tells AI what to read

97% of llms.txt files are never opened by any bot.⁷

The file sits on your server and nothing reads it. Google says you don't need special "AI text files,"⁶ and Google's John Mueller has called llms.txt "not done for search."

Do this instead: add one if you like, it's harmless. Then spend the time on pages crawlers can actually read.

Myth 4: Longer pages, or bite-sized chunks

Length has basically no link to AI citations.

Ahrefs found almost zero correlation between word count and AI Overview citations (0.04).⁸ The 90-day ChatGPT study found the same.¹ The opposite fad, chopping pages into tiny chunks "for AI," doesn't work either. Google's Danny Sullivan said in January 2026 that Google doesn't want content split into "bite-sized chunks."⁹

Do this instead: write each section as long as the question needs. A definition might be two sentences. A how-to might be a page. Keep related ideas together.

Myth 5: FAQ blocks, lists and tables

Formatting looks like it works until you compare pages on the same site.

Sites with FAQ blocks seemed to get cited more. But comparing pages within the same site, the effect vanished. Lists, tables and headings showed no effect at all.¹ Strong sites just tend to have both good formatting and good content. A 252,000-trial study across six AI models agreed: relevance matters, formatting barely does.¹⁰ And removing FAQ schema didn't change traffic in SearchPilot's tests.¹¹

Do this instead: format for readers so they can scan. Put your effort into answering the question better than anyone else.

Myth 6: Keyword stuffing and jargon rewrites

Use the words your reader typed, not fancier ones.

AI search matches pages to the words in the question. Keyword stuffing scored worse than doing nothing.² Rewriting into "technical terms" cut how often pages were found by up to 14%, because it removes the words people actually search for.⁴

Do this instead: check how people phrase the question (search suggestions, Reddit, your support inbox) and use those words.

Myth 7: Bumping the date to look fresh

Google calls fake dates manipulation, in writing.

Google's guidance names "changing the date of pages to make them seem fresh when the content has not substantially changed."¹² Freshness does help a little: pages cited by AI are about 26% fresher than average search results. But the average cited page is still 2.9 years old.¹³ Researchers found fake dates can fool AI models,¹⁴ but that's a weakness, not a strategy.

Do this instead: update the content (new stats, new examples, fixed claims), then update the date.

Myth 8: Mass-producing pages across many sites

AI writing isn't the problem. Pages that add nothing new are.

Across 600,000 pages, how much was AI-written had no link to ranking.¹⁵ What Google penalises is "using generative AI tools to generate many pages without adding value."¹⁶ Google wiped hundreds of these sites from search in March 2024,¹⁷ and recovery is rare. None of 200+ sites hit by the helpful content update had recovered months later,¹⁸ and after August 2024 only 22% got back even a fifth of their traffic.¹⁹ Leaked Google documents suggest it also tracks how far a site drifts off its main topic.²⁰

Do this instead: publish fewer pages, each with something new: your data, your examples, your opinion. Stay on your topic.

Myth 9: Client-side rendering and blocking AI crawlers

If the crawler can't read your page, nothing else here matters.

Most AI crawlers (GPTBot, ClaudeBot, PerplexityBot) read your raw HTML and don't run JavaScript. Only Googlebot does.²¹ If your content loads with JavaScript, those bots see a blank page. Blocking AI crawlers in robots.txt or your firewall also makes you less likely to show up in AI Overviews.²²

Do this instead:

Open your page, right-click, "View source." If your main text isn't there, AI crawlers can't see it.
Check robots.txt for rules that block GPTBot, ClaudeBot or Google-Extended.
Check your CDN or firewall bot settings (Cloudflare and similar often block AI bots by default).

Myth 10: One ChatGPT check tells you your visibility

AI answers change almost every time you ask.

Fewer than 1% of repeated runs gave the same list of brands.²³
Only 14% of pages ChatGPT cited were still cited ten weeks later.¹
In one test, 57.8% of ChatGPT answers didn't search the web at all.²⁴
ChatGPT's Reddit citations fell from about 60% to 10% of answers overnight in September 2025.²⁵

Do this instead: ask the same questions many times, with a few different phrasings, and track your share over weeks. One screenshot proves nothing.

What actually works

Six things hold up, and none of them is a formatting trick.

What to do	Why
Rank in regular search	Being found explains ~32% of citations vs 2.8% for everything on the page¹
Title that matches the question	About +14% more citations on the same site¹
Answer in the first paragraph	About +15% more citations¹
Concrete facts: prices, specs, real dates	Wins consistently across 252,000 trials and six AI models¹⁰
Get your brand mentioned on other sites	Tracks AI visibility three times more closely than backlinks²⁶
Pages readable without JavaScript	Most AI crawlers don't run it²¹

Cited is not the same as clicked

Getting cited helps, but it won't bring back the clicks AI took.

Seer Interactive found clicks on informational searches average 3.35% with no AI Overview, 2.07% when your brand is cited in it, and 0.94% when you're left out.²⁷ So being cited roughly doubles your clicks versus being left out. People click links inside Google's AI summaries on only about 1% of visits.²⁸

Being known also isn't the same as being recommended. In a study of 112 startups, ChatGPT knew them 94–99% of the time when asked by name, but suggested them in only 3.3% of "best X" questions.²⁹

The difference is how often you're talked about elsewhere. Mentions of your brand on other sites track AI visibility far more closely than backlinks do.²⁶ That's where to spend time once your pages are solid.

On your own site, the work is simple to state: titles that match the question, openings that answer it, and facts competitors don't have. That's the bar PostKing writes to, drafting from a brand's own verified facts instead of paraphrasing what already ranks.

Quick ways to make sure an LLM never cites you

Load your content with JavaScript only. AI crawlers won't see it.²¹
Block AI crawlers by accident. One robots.txt line or firewall setting is enough.
Rewrite pages into jargon. You lose the words people search for.⁴
Bump dates without real changes. Google calls it manipulation.¹²
Publish near-identical pages at scale. Google penalises it, and sites rarely recover.¹⁶

FAQs about How Not to Get Cited by LLMs

What does "retrieval" mean?

It's the search step. Before ChatGPT or Perplexity answers a question that needs the web, it searches, reads a few results, and writes its answer from those. If your page isn't in those results, it can't be cited.

Is GEO different from SEO?

Less than most guides suggest. AI search leans on regular search results, so ranking well is still the biggest lever. The extras that matter are answer-first writing, original facts, and being mentioned on other sites.

Does schema help you get cited by ChatGPT or AI Overviews?

No. In the best test, AI Overviews citations dropped slightly after adding schema, and ChatGPT barely moved. Keep it for Google rich results.

Is llms.txt worth adding?

It's harmless but almost never read: 97% of these files get zero visits from bots. Low priority.

Does Google penalize AI-written content?

Not for being AI-written. Google penalises lots of pages that add nothing new, whoever wrote them.

What should I do first?

Check that AI crawlers can read your pages (View source, robots.txt, firewall). Then rewrite your top pages so the title matches the question and the first paragraph answers it.

Sources

About Dite Gashi

Author

GTM Engineer, 15 years as a full-stack developer. Now cracking the distribution code.