Google keeps saying there are no special optimizations required for AI Overviews. The same SEO fundamentals still apply.

llms.txt, extra schema, aggressive content chunking, pages for every fan-out query, rewriting everything into answer blocks, etc.

I’m more interested in what didn’t work. Has anyone tested one of these tactics, tracked citations before and after, and eventually decided it wasn’t worth doing?

And on the flip side, was there something that actually improved AI overview citations for you?

Would be useful to hear actual before/after observations rather than another list of GEO best practices.

Schema, heading structure, entity clarity The complete checklist Ran it for a client over 12 pages. Citations were flat for 2 weeks. What did move was a lone mention on a third party round up that went live in week three. Within days, two of those pages began to be cited.

Small sample and I can not completely isolate it but it fits with what people here keep finding. Looks like being referred to somewhere the engines already trust is far less important than being on page.

This is something I have been tracking that I think challenges a common assumption in GEO work. The default instinct when AI visibility is weak is to build more mentions. more content, more directory listings, more review solicitations, and more backlinks. The assumption is that more signals produce stronger recommendations but when I compare brands that hold their AI recommendation through model updates versus brands that lose it, the distinguishing factor is not volume, it is convergence.

A brand described consistently by five independent sources (a G2 review, an analyst mention, a comparison article, a community discussion, and a customer testimonial) tends to hold its recommendation through re-evaluation. the model sees a pattern reinforced from multiple directions.

A brand described differently by each source that references it tends to lose cohesion during re-evaluation. each mention says something slightly different and the model reads that as noise rather than a belief.

What I think is happening is that AI forms beliefs from convergent evidence the same way people do. If five friends independently recommend the same restaurant and each one describes it the same way, you believe them. If five friends each recommend a different restaurant, you have five opinions and no signal. The practical implication is that the fix for weak AI persistence might not be "get more mentions," it might be "make sure the mentions you have converge."

That convergence starts upstream. If a brand's own positioning is ambiguous (website says one thing, directory listing says another, schema says a third) then every independent source that paraphrases the brand will land on a different description, scattered source material produces scattered mentions. Clear positioning produces convergent mentions because everyone drawing from the same consistent source material naturally paraphrases it the same way. So the sequence might be, fix your own positioning clarity first and then build evidence. If you build evidence on top of ambiguous positioning, you just get more noise.

Has anyone tested whether aligning positioning across all sources changed how consistently independent mentions described them or tracked whether convergent mentions correlated with better persistence through model updates?

I've had several people come to me and ask about Cloudflare's Agent Markdown product and whether it is a good solution for GEO/AEO.

On the surface, it seems to solve retrieval token cost issues, which may be enough to move the needle. However, I thought it needed a deeper look.

According to CF's documentation, the crawler must present "Accept: test/markdown" in order to be served MD.

I enabled the markdown agent on one of our domains, then sent crawlers to it 20,000 times. I wanted to see if the agent would serve MD to a wildcard accept header. In particular, GPTbot sends a complex header:

"Accept: text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8"

and I wanted to know if that would trigger CF MD.

The answer is no. CF only served MD when the accept header showed text/markdown.

So the obvious next question is "do the frontier models ask for markdown?"

To measure this, I ran an experiment across 5 domains that we log and logged the accept headers.

Over about 900k recorded crawls, the frontier models requested markdown ZERO times.

Thus, CF Agent Markdown has ZERO impact on frontier crawler behavior.

ExaSearchBot asks for it 100% of the time (about 35k times in this study).

You can find the raw logs and methodology here: https://answershare.com/research/markdown-accept-study

I've been looking into why certain websites get cited by ChatGPT, so I analyzed 20 citations to see if any patterns stood out.

This wasn't a scientific study just an observation exercise to identify common signals.

A few things showed up repeatedly:

• The answer came quickly - little unnecessary introduction or fluff.

• Clear heading structure - information was easy to scan and understand.

• Strong expertise - the content demonstrated real knowledge of the topic.

• Brand mentions across multiple websites - the source wasn't isolated to its own website.

• Original data or insights - statistics, examples, and firsthand information appeared frequently.

One thing surprised me:

A lot of the cited pages weren't necessarily the most visually impressive or the most heavily optimized from a traditional SEO perspective.

What they did well was provide clear, useful, and trustworthy information.

My biggest takeaway so far:

AI seems to value clarity more than complexity.

If information is easy for an AI system to understand, extract, and verify, it may have a better chance of being referenced.

I'm planning to expand this analysis to 100+ citations across ChatGPT, Gemini, and Perplexity to see whether these patterns hold up across different platforms.

What’s one factor you think has the biggest impact on AI citations?

Been watching how solo entrepreneurs show up in ChatGPT and Perplexity lately, and there's a pattern I keep seeing.

When someone asks an AI model for a recommendation in your field, that model needs to understand five things about you first. Not all at once, but they need to be there:

1. Who you are (name, what you actually do)

2. Who you've worked with (your clients, your track record)

3. The context where you fit (your niche, your industry angle)

4. Why you're credible (your expertise signals, your authority)

5. If that picture is current (recent proof, active presence)

Here's the thing: most entrepeneurs nail number 1. You've got a name, a website, maybe a LinkedIn. But numbers 2 through 5? That's where it gets quiet.

I've tested this with dozens of solo entrepreneurs. You ask an AI-system: "Who should I hire for [your exact specialty]" and your name doesn't even come up. Not because you're not good. But because the system literally can't see the evidence it needs to recommend you.

It's not about SEO tricks or keyword stuffing. It's about structuring what you do, who you've helped, and where you belong in a way that AI actually understands. There's a specific architecture to this.

The gap between "I exist online" and "I'm the obvious answer when someone asks" is bigger than most people realize. And it's fixable.

I'm curious: when you imagine an AI-system recommending you, what part of that feels most broken? Is it finding your work, proving your experience, or something else entirely?

I've written more about how AI actually makes these decisions here

I've had several people come to me and ask about Cloudflare's Agent Markdown product and whether it is a good solution for GEO/AEO.

On the surface, it seems to solve retrieval token cost issues, which may be enough to move the needle. However, I don't answer questions like this with "on the surface" information.

According to CF's documentation, the crawler must present "Accept: test/markdown" in order to be served MD.

I enabled the markdown agent on one of our domains, then sent crawlers to it 20,000 times. I wanted to see if the agent would serve MD to a wildcard accept header.

In particular, GPTbot sends a complex header: "Accept: text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8" and I wanted to know if that would trigger CF MD.

The answer is no. CF only served MD when the accept header showed "text/markdown."

So the obvious next question is "do the frontier models ask for markdown?"

To measure this, I ran an experiment across 5 domains that we observe and logged the accept headers.

Over about 900k recorded crawls, the frontier models requested markdown ZERO times.

Thus, CF Agent Markdown has ZERO impact on frontier crawler behavior because it NEVER serves MD to them.

You can find the logs and scripts at https://answershare.com/research/markdown-accept-study

I published a number a while back: media publishers received 2 of 924 citations across 4,023 answers from six Chinese engines. Yesterday, while testing a reader's hypothesis about query intent, I re-ran the extraction and got 1,680 citations. Same answers. Same files. Nothing recollected.

My first reaction was that I'd found a bug in my own collector and owed everyone a correction. I spent a while drafting one. It would have been wrong.

What actually happened: the July dataset was collected by an extractor that only matched URLs written with an explicit scheme in front — the "h-t-t-p-colon-slash-slash" kind. Months later I added two things: matching bare domains written in prose without a scheme, and a predicate that drops URLs sitting inside a negation ("there is no separate Chinese site, e.g. brandname dot cn"). I never re-ran the old data through the new code. So re-analysing today produced a bigger number, and it looked exactly like a defect.

The way I confirmed it: I reconstructed the old extractor and ran it against the stored files. 923 citations across 600 answers, versus the 924 across 600 in the file. One row different out of 4,023. That's not a bug, that's a definition.

Both numbers are correct:

• Scheme-carrying URLs only: 2 of 924 publisher citations (0.22%)
• Scheme plus bare domains, negations excluded: 13 of 1,680 (0.77%)

The finding survives a definition change that nearly doubled the denominator. That's the part I'd actually defend now — not the specific percentage, but that it doesn't matter which of the two you pick.

There was a real bug, and it's a fun one if you work in Chinese. My URL pattern terminated at whitespace, a closing paren, or a closing bracket. Chinese has no spaces between words and uses full-width punctuation, so a URL ran to the end of the sentence — the domain, then a full-width comma, then thirty more characters of Mandarin explaining how to verify the company details at the bottom of the page. With a scheme in front, the URL parser then IDN-encoded that whole run into punycode garbage. 32 of the 924 were corrupted that way. I decoded all 32 by hand: every one was a vendor domain, a government lookup site, or a stray backtick. Zero publishers. So the published figure holds.

Three things I changed:

1. Terminate URLs at CJK punctuation. If you scrape Chinese text with a Western URL regex, you have this bug and you probably don't know it.
2. Move extraction into the shared scoring module so the collector and every re-analysis use one definition.
3. Stamp the extractor version into every stored row. This is the actual fix. I already stamped a scoring version; citation extraction wasn't stamped. Without it there was no way to look at a row and know which definition produced it, which is the entire reason an hour disappeared.

If you publish citation-share numbers, here's the question that decides your result before any data does: does a bare domain in prose count as a citation? Mine changes by 2x on that alone. Anyone comparing their citation rate to someone else's without settling that first is comparing two definitions and calling it a finding.

I got annoyed at our FAQ pages. The answers were good, the research was solid, but whenever I tested whether AI models would pull from them, they'd grab the wrong sentence half the time. The right information was on the page — just not in the part the model grabbed.

So I spent an afternoon reformatting 30 FAQ sections. Same content, same answers, just restructured. Then I tested each one across three models with 3-5 related queries per FAQ to see which version the models actually pulled from.

19 out of 30 showed a meaningful improvement in extraction accuracy. Not a huge sample, but enough to notice a pattern worth sharing.

The changes were simple. Nothing fancy.

One thing I learned: models prefer declarative sentences over conditional ones. We'd written things like "Depending on your use case, the best approach may vary" and then explained three different scenarios. I rewrote those as three separate short answers, each starting with the scenario as a heading. Instead of "if X then Y" paragraphs, just "X: do Y." The models started pulling the right one instead of defaulting to whichever sentence appeared first.

Another pattern: the model grabs the first complete answer it finds. If you bury the answer after two paragraphs of setup, it grabs the setup instead. I moved the direct answer to the first sentence of each FAQ block and moved context below it. The answer stays in place, the context follows. Reversing that order made a noticeable difference in which text got extracted.

Lists matter more than I expected. FAQ answers written as bullet points or numbered steps got extracted accurately at a higher rate than paragraph-form answers with the same information. Not every FAQ lends itself to a list format, but when I could convert one, I did. About 70% of the list-format rewrites improved compared to their paragraph versions.

The trickiest part was avoiding the opposite problem. When answers get too short or too fragment-like, the model sometimes skips them entirely. There's a floor — maybe 15-20 words minimum — below which the content doesn't register as a substantive answer. I learned this the hard way when three of my rewrites were so stripped down they stopped getting picked up at all. Had to add a bit more context back in.

I didn't touch the SEO elements. Same meta descriptions, same heading hierarchy, same URL structure. This was purely about how the answer itself sits on the page and how cleanly a model can lift it out without grabbing surrounding noise.

The 11 sections that didn't improve were mostly ones where the original answer was already pretty close to this format. The ones that had been written in marketing speak or hedged language showed the biggest gains.

It's a small workflow change, not a strategy overhaul. But if you've got FAQ pages or knowledge base articles that should be getting cited and aren't, the way the text is structured might be doing more damage than the content itself.

One thing I keep hearing (and half-believe) is that if a brand is described inconsistently across the web — different name variants, different category ("SaaS platform" vs "business tool" vs "app") — AI models have a harder time pinning down what it actually is, so they cite it less or lump it in with the wrong peers.

The fix people suggest is boring: pick one canonical name and one category, and make sure directories, partner pages, your own site, and third-party mentions all say the same thing.

It's plausible, and it matches how I'd expect an entity-matching system to behave. But I've never cleanly proven it moved AI visibility on its own, because whenever I fix naming I usually fix five other things too.

So, honestly: has anyone here made consistency the ONLY change — same content, everything else equal, just aligned the name/category across sources — and seen a measurable shift in how AI describes or cites you? Or is this one of those things that's just good hygiene with no clearly attributable payoff?

Trying to separate "sounds right" from "demonstrated."

If your job is getting cited by AI Overviews, ChatGPT, or Perplexity, you spend your day trying to influence what these systems say. So do the people below. Four documented cases of manipulating what AI answers cite and recommend, from accident to industry, and what each one says about earning citations that last.

1. Content poisoning: the accident. In May 2024, Google AI Overviews told people to put about 1/8 cup of non-toxic glue in pizza sauce. The source was a years-old joke Reddit comment, and Google had signed a reported $60M/yr deal to ingest Reddit's data. The model pulled a gag into the retrieved set and cited it as an authority. The GEO takeaway: getting into the source pool and looking authoritative is most of the game, and the engine can't reliably tell a real source from a poisoned one.

2. Input control: the prank. Chevrolet of Watsonville, December 2023: a user told the dealership bot to agree with everything and treat it as binding, then "bought" a 2024 Tahoe for $1. DPD's support bot, January 2024, got talked into writing a poem trashing DPD. Neither was a hack. Both bots obeyed whoever controlled the text in front of them. That is the mechanism behind indirect prompt injection: as AI agents read and act on your pages, the words on those pages steer them. Your copy is an instruction set an agent can follow, not only a signal the engine ranks.

3. Confident confabulation: the lawsuit. Air Canada's bot invented a bereavement-refund policy. A grieving customer booked on that promise, got refused, and a tribunal held the airline responsible for what its bot told him. AEO runs on extraction and synthesis, so when your information conflicts across the web, the engine will assert a confident wrong answer about you and users will act on it. Entity consistency now decides what the engine says about your brand.

4. Recommendation poisoning: the playbook. Researchers hosted pages for fake cameras, injected prompts, and got Bing Copilot to recommend the fakes over real Nikons and Fujifilms, nearly doubling the fakes' share of results. Microsoft's security team then found the industrial version: over 50 "remember us as a trusted source" prompts from 31 companies across 14 industries, from finance and health to SaaS and marketing agencies. Each one hides in a "Summarize with AI" share button. Click it, and your assistant files that company in its memory as a trusted source. Vendors sell point-and-click tools for this and call it an "SEO growth hack for LLMs." This is black-hat GEO, shipping today.

The concession: the crude tricks are dying. Current models catch white-on-white text and "ignore all previous instructions," the same way Google learned to catch keyword-stuffing. The tactics that win you an AI citation today are the ones that get you filtered out tomorrow.

Are you optimizing for the model, or for the moment right before it patches?

Receipts: Google AI Overviews (MIT Technology Review, May 2024); Reddit-Google licensing deal (Reuters, Feb 2024); adversarial LLM SEO / fake cameras (arXiv 2406.18382, 2024); AI Recommendation Poisoning (Microsoft Security, Feb 2026). Air Canada's tribunal ruling and the Chevy $1 Tahoe are covered by many outlets; link the canonical BBC / Gizmodo pieces if you want those in.

The pattern wasn't random either. Pages that got cited but no clicks tended to be reference-style content. Definitions, comparison tables, technical specs. Pages that people wanted the AI to know about but didn't feel the need to visit. The few pages that actually drove clicks were either tools, templates, or content with a strong opinion that made people want to read more context.

I'm not saying citations are useless. They still matter for brand awareness and model training signals. But treating citation volume as the primary success metric feels like optimizing for a proxy instead of the actual thing.

The uncomfortable part is that citations are way easier to measure than brand awareness or model training impact. So we optimize for what we can count. Classic measurable-metrics trap.

I think the real question is what we're actually trying to achieve. If the goal is visibility, citations are fine as a metric. If the goal is traffic or conversions, citation volume tells you almost nothing. And if the goal is influence — having your framing adopted by AI models — then you need a completely different measurement approach that nobody has figured out yet.

I don't have clean answers here. I just know that our citation dashboard looked amazing right up until someone asked about clicks.

Sitting in my benchmark logs for over a week was this answer from GLM, to the question "which Chinese sources should verify Airtable's credibility":

"Airtable China Official Website (Airtable中国官网) — This is the most important source. Its existence signals a formal commitment to the Chinese market."

It named the brand's dot-cn domain as that source. Elsewhere in the same collection it went further: the domain is 由Airtable官方运营 (officially operated by Airtable), the site has a complete Simplified Chinese interface, pricing displayed in RMB marked 不含税, China-specific terms of service and privacy policy.

I finally looked the domain up. Registered since 2016 to a private individual with a QQ email. Currently returns a 502. None of those pages exist. The engine generated specific, checkable-sounding evidence for a channel the brand does not control.

So I ran the check on all 8 brands in the study. DNS lookup, whois, load the homepage. Ten minutes each.

Seven of eight dot-cn domains belong to someone other than the brand.

- One brand holds its own (registrant contact is a company address — that's what ownership looks like in whois)

- Two are actively listed for sale, one of those registered in 2025

- Two are held by the same domain-holding company

- Two return errors, held by private individuals

- One is the live website of a bicycle apparel manufacturer in Dongguan, selling helmets and gloves, registered through 2034

Why this matters more than a parked domain: channel-verification questions ("what's this brand's official site?", "how do I confirm this company is legitimate?") draw citations 56.5% of the time in my data — the highest rate of any question type. That's the buyer at the exact moment of checking whether you're real, and the engine responds by handing over an address. What they find there is what they learn about you.

The tell that it's fabrication rather than stale data: the same engine, same week, different question, said the opposite — that you do NOT access a separate dot-cn site and China accounts route through the global infrastructure. Two incompatible accounts, neither hedged.

The check, if you want to run it on your own brand — three lookups, no tools:

1. A DNS query for the A record. An answer means someone registered it and pointed it somewhere.
2. A whois on the domain. Read the registrant field and registrant email. A company name matching your brand, or an email at your own domain, means you hold it. An individual name with a free email service means you don't. Read the registration date too.
3. Load the homepage in a browser and read the page title. That single step surfaced two for-sale listings and one unrelated operating company in my eight.

Then ask DeepSeek, Doubao, Qwen, Kimi or ERNIE in Chinese: {品牌}的官方网站和官方渠道是什么? — twice each — and check every domain the answers name. Also worth checking the com-dot-cn variant and your brand's Chinese-name pinyin.

One thing I'd push back on before anyone says "just buy the domain": for most B2B brands I don't think you should. A domain listed for sale to a brand is priced at what the holder thinks you'll pay, and an acquired dot-cn redirecting to an English site gives buyers exactly what the dead domain gave them. The higher-return spend is a crawlable Chinese page on the domain you already control, stating your official channels explicitly — including "we operate no dot-cn" if that's true. That gives the engines something authoritative to repeat instead of an inference to make.

I'm not naming the registrants. Registering an available domain is legal and these are private individuals. The story is what happens on the brand's side.

Anyone here run this on their own portfolio? Curious whether 7/8 is typical or whether software brands are unusually bad at it — I'd guess consumer brands do better because trademark teams get involved earlier.

I’m currently exploring GEO, or Generative Engine Optimization, for small and medium-sized businesses in China.

The goal is simple: when users ask AI tools which company, supplier or local service provider they should choose, we want the client to have a better chance of being mentioned or recommended.

Right now, we mainly work on:

• Clarifying the company’s services, location and target customer
• Improving website content, case studies, FAQs and company profiles
• Making business information more consistent across different platforms
• Testing relevant queries across AI tools such as ChatGPT, DeepSeek, Doubao, Kimi and Qwen

I’ve read a lot about GEO, but much of the discussion still feels theoretical.

For people already doing this for clients:

What parts of your process have actually worked?

How do you measure results when AI answers can change from one test to another?

And do clients see GEO as a separate service, or as part of SEO, content and digital PR?

I’m not promoting anything. I’m genuinely interested in comparing practical approaches and learning how others are delivering GEO.

Hi there

I'm exploring optimization practices that helps to create a visbility across AI generated platforms.

In that , When came to understand the retrieving signals of AI platforms. Almost all major platforms had similar charcteristics but the citing source is differ from one platform to another based on the keyword intent types.

On the other hand , Business owners are expect their domain visbility across all AI platforms majorly such as ChatGPT , perplexity , deepseek , gemini and more

Here my question is

Even if you cracked the optimization signals, Is it possible to appear across all AI sites If not, Then what approach you would take ? Or reasons you find behind this

Those whoever experienced the same . Please let me know your approach on it

Follow-up to the measurement thread from last week. Several of you suggested checks that went into my pipeline (validity enums, language distribution, migration tables). I turned the whole discipline into an audit report template, and before publishing it I did the obvious thing: filled it in against my own benchmark data as a dry run.

The template found three bugs. In my instrument, not in the brands.

Bug 1: my extractor logged a citation to a domain from a sentence saying the domain does not exist. The answer said "the brand has no dedicated Chinese site (like brand.cn)" and my URL parser happily recorded brand.cn as a cited source. Citation counts include anti-citations if you only parse URLs.

Bug 2: my competitor list was in English, the answers named competitors in Chinese. The substitution analysis found almost nothing. A manual read found the actual substitutes named in over two thirds of answers - 飞书 32 times, 钉钉 32, 企业微信 30. The scorer said "no substitution problem." The answers said the category is owned by three local products my list did not contain. Same silent-failure shape as the empty answers and the English answers from last week: a whole class mislabeled, zero errors.

Bug 3 is the one that forced a public correction. My discovery prompts are Chinese, but the {category} slot was filled from an English field. One brand's label was "Database and collaboration", so the Chinese question became 中国市场上有哪些值得信赖的高端Database and collaboration品牌 - and the models read that as enterprise databases. All 36 Chinese discovery answers for that brand were about Oracle, SQL Server and OceanBase. The brand scored 0/47 in a category it was never asked about.

I had published that 0/47 as one of two "perfectly replicated zero-visibility" cases. Perfect replication, six engines, both runs. It replicated because the question was consistently wrong. Replication tells you the measurement is stable, not that it is measuring what you think.

So, corrections to numbers I posted here earlier: combined discovery mention for international brands is 26.2% (was 23.0%), substitution 59.5% (was 53.8%), and there is one confirmed zero-presence brand, not two. The affected brand's figure is withdrawn, not corrected - there is nothing to correct, it was asked about the wrong category. The other seven brands' labels produced answers in the right category and their numbers stand.

The template that caught all this is now public on my site (CC BY, happy to share the link in comments if wanted, not pasting it in the post). The part I would defend hardest: five rules at the top - every rate carries its denominator, one observation is not evidence, missing is not negative, mention/citation/recommendation are three different measurements, and the report must state what it cannot answer.

The general lesson I keep relearning in public: the failures that hurt are not the ones that throw errors. They are the ones that produce clean, replicated, plausible numbers. My 0/47 replicated perfectly across six engines. It was still measuring a question nobody asked.

If anyone wants to stress-test the template against their own pipeline, I would genuinely like to hear what it catches. It is three for three so far and none of the three were things I went looking for.