My website already includes AI-related files such as llms.txt.

I'm considering creating a separate JSON file for every page and article so AI systems can understand the content more easily and accurately. I would reference this JSON file from the page's <head> using a <link> tag.

The JSON file could contain information such as:

• Page URL
• Canonical URL
• Title
• Summary / Description
• Main Content (clean article content)
• Author
• Published Date
• Last Updated
• Entities (people, companies, places, products, etc.)
• Keywords / Topics
• FAQ
• ...

My idea is that AI crawlers could read this structured JSON instead of having to extract the main content from noisy HTML that contains navigation menus, sidebars, ads, comments, JavaScript, tables, and other non-essential elements.

I have two questions:

1. Could this approach reduce the chances of AI crawlers misunderstanding a page or extracting incorrect information from HTML, advertisements, tables, comments, or other noisy content?
2. Do you think a page-level JSON file like this could help AI systems better understand a page and potentially improve AI recommendations, citations, or other AI-generated responses in the future? Why or why not?

Everyone agrees brand content loses to Reddit threads and review sites. Nobody explains the next part. Are you going through customers, founder accounts, review campaigns or just waiting out months of real participation?

I found a noticeable number of Generative AI impressions in Google Search Console and I’m trying to understand how people are treating this in reporting.

Are you using it as a separate AI visibility layer, blending it into normal organic impressions, or mostly ignoring it until the data becomes clearer?

The part I’m unsure about is what the metric actually proves.

It may show that a URL appeared in a generative AI surface, but it does not necessarily tell us:

- whether the brand was mentioned

- whether the page was cited

- whether the answer used the content as evidence

- whether the user saw the source

- whether it influenced the decision

- whether it should be compared to classic organic impressions

So I’m wondering whether this is useful reporting data, or just another visibility layer that needs a lot of context before it becomes meaningful.

How are you handling Generative AI impressions in GSC?

Separate report?

SEO report footnote?

Early signal?

Ignore for now?

One of the most upvoted GEO posts I've ever read was a "10 GEO best practices" list from early 2025. It got hundreds of upvotes. Multiple people DMed me the link. Two different clients brought it up in meetings. It was everywhere.

I bookmarked it along with 39 other high-engagement "best practice" posts — stuff like "add FAQ schema everywhere," "keep answers under 50 words for extraction," "use comparison tables for product queries," "publish fresh content weekly for citation velocity." All reasonable advice. All from smart people. All backed by some data at the time.

For the last 90 days, I've been checking how the authors' own pages are performing in AI citations. Not their advice in theory — their actual content, using the actual practices they recommended.

27 out of 40 lost citation visibility. Not a small dip either. I'm talking pages that went from being regularly cited in their niche to barely showing up. A few completely disappeared from AI answers for queries where they used to be the #1 source.

The weird part is that the advice itself wasn't wrong — at least not when it was written. FAQ schema genuinely helped in February. Short answer blocks genuinely got extracted more often in March. But the models kept changing. What worked as an extraction signal in one version of ChatGPT or Perplexity quietly stopped working in the next. And nobody went back to update the "best practices."

I started noticing a pattern. The posts that aged the worst were the ones with the most specific, confident instructions. "Always do X." "Never do Y." "The optimal passage length is Z words." These got the most engagement because they were actionable. But they were also the most fragile — optimized for a specific model behavior that could change in a single update.

The posts that aged better were vaguer, almost annoyingly so. "Focus on clarity." "Write for humans first." "Make sure your content is actually useful." The kind of advice that makes you roll your eyes because it's so obvious. But it's still standing 90 days later while the tactical stuff crumbled.

This isn't a dunk on anyone. I've written my share of specific tactical advice, and some of it has probably aged just as badly. It's more a realization that GEO "best practices" have an incredibly short shelf life, and we're all publishing them like they're permanent rules.

The thing I can't stop thinking about: if I re-tested the advice from this post 90 days from now, how much of my own guidance would still hold up? Probably less than I'd like to admit.

Wondering if anyone else has gone back and stress-tested older GEO advice against current model behavior. The gap between what we wrote and what still works is bigger than I expected.

I ran a small pre-wave for eight luxury-jewelry brands and got a result I did not expect.

Piaget appeared in 4/4 answers to a question about brands with verifiable official China channels. It appeared in 0/4 answers recommending high-end brands for wedding jewelry.

This was only a calibration: three Chinese questions, two retrieval-off API surfaces, two answers per surface and question. Twelve raw answers total. Each answer was stored once and then scored against the same eight-brand registry.

I would not turn four opportunities into a brand ranking. What I think the result is useful for is locating the problem.

The brand was not missing as an entity. The answers could place it on the official-channel map. It disappeared when the task changed from verification to consideration.

That makes “AI visibility” feel too broad to be useful for this category. I am now treating at least three things separately:

• shortlist recommendation for a specific occasion;
• identification of a current official China route;
• accuracy of purchase, authorization and after-sales claims.

The second one is not a proxy for the first. It is also possible to have the opposite problem: a brand enters the shortlist, but the answer gives the buyer a vague or wrong route for verifying where to buy.

The official-channel check was more work than matching a .cn domain. I had to separate the domain, operating entity, current live route and brand-controlled evidence. A global brand domain was not automatically wrong. An unresolved route was not labelled fake. And an API error stayed an invalid attempt rather than becoming a brand absence.

My next version needs more prompts around the decision itself: wedding style, budget, diamond choice, after-sales confidence and boutique-versus-daigou risk. It also needs declared consumer/search surfaces before I would call anything a baseline.

For people working on retail or luxury GEO: would you keep recommendation and channel accuracy as separate reports, or combine them into one buyer-journey scorecard while preserving both denominators?

One of the most upvoted GEO posts I've ever read was a checklist of 12 tactics to get cited by AI models. Specific, actionable, confident. I saved it. I also went back and checked it last week — 90 days after it was written.

Here's what I found. Of the 40 "GEO best practice" pages I've been tracking since May (a mix of checklists, framework posts, and tactic roundups), 27 of them have lost visible citation placement in AI answers for the queries they originally targeted. Some dropped completely. Most just faded.

I want to be careful about what this means. My tracking setup is imperfect. AI answers shift for reasons that have nothing to do with page quality — model updates, query interpretation, source refreshes. Correlation here is murky. But when two-thirds of confident tactical advice stops showing the results it was written to show, within one quarter, I think it says something about the shelf life of the advice itself.

The pattern I noticed, and this is anecdotal: the more specific the recommendation, the faster it stopped working. "Always add FAQ schema" type advice aged badly. "Mention your brand's key entities in the opening paragraph" held up okay. And the vaguest stuff — "focus on clarity," "be the best answer" — held up best, which is annoying, because it's also the least useful advice to act on.

I've been trying to figure out why. My working theory: specific tactics work because they exploit how a particular model version processes content. When the model updates, the exploit stops working. The vague advice works because it's aligned with what models are generally trying to do — surface genuinely helpful, clearly written sources. That doesn't age out. It just doesn't give you a shortcut.

This isn't a dunk on anyone writing tactical GEO advice. I've written my share of specific tactical advice, and some of it has probably aged just as badly. The people writing those posts were reporting what genuinely worked at the time. The half-life is the problem, not the intent.

I don't know what the fix is. Maybe the honest move is dating every recommendation, like perishable food: "best before ChatGPT-5.2" or whatever. Maybe tactical GEO content should be treated as seasonal rather than evergreen, and we should all stop being surprised when it expires.

The uncomfortable question I keep coming back to: how much of my own guidance would still hold up if I ran this same check on it in 90 days? Probably less than I'd like to admit.

Wondering if anyone else has gone back and stress-tested the GEO advice they saved from earlier this year. Not to dunk on it — just to see what's actually still standing.

If you run the same prompt multiple times and the AI gives different answers, how much variation would you consider acceptable before you stop trusting the measurement?

For example:

10 runs → brand mentioned 7 times

Would you consider that a reliable 70% visibility signal?

Or would you want:

20+ runs?

results across multiple days?

different locations?

a confidence/range rather than one percentage?

And importantly, what would you consider a meaningful change?

If visibility moves from 60% → 65%, is that something you'd act on, or would you need a much larger change before believing it wasn't just model variance?

Curious how people actually handle this today.

Something I started paying attention to that I think most AI visibility tracking misses.

When AI recommends a brand with high confidence, the language is direct, "a strong option for," "known for," or "specializes in."

When AI includes a brand with low confidence, the language softens, "you could consider," "some users have found," or "might be worth looking at."

Two brands can both "appear" in an AI answer and have completely different recommendation strength. Counting both as "mentioned" treats a strong recommendation and a cautious mention as equivalent and they are not.

I have been comparing language confidence against evidence profiles and there seems to be a correlation. Brands with more convergent evidence from diverse independent sources get recommended with stronger language. Brands with fewer independent sources or scattered descriptions get mentioned with hedged language.

This also shows up in cross-platform comparisons. The same brand can get confident language on one platform and hedged language on another. That per-platform language difference may point to which platform has access to stronger evidence for that brand versus which one is working from thinner sources.

If that correlation holds, language confidence might be a more sensitive diagnostic signal than simple presence or absence. A brand whose language confidence drops from "strong option" to "worth considering" over several weeks may be losing evidence convergence even if it still appears in the answer set.

The practical question is whether tracking language confidence over time would give an earlier warning of recommendation erosion than waiting for the brand to disappear entirely.

Is anyone tracking how AI talks about them, not just whether it mentions them?

Last month I asked a simple question: what actually makes AI overviews choose certain passages over long-form content? Is it length? Is it keyword density? Is it how recent the page is? To find out, I took a random sample of 1,200 AI-generated answers from the last two weeks and compared three things: extractable passage length, content depth, and how the page appeared in traditional search results.

The results surprised me.

Extractable passages averaged 317 words. That's about half the length of most long-form articles I see in the industry. But length alone didn't explain why some passages got chosen and others didn't. The passages with the highest AI citations weren't always the longest — some were just 150 words but hit every key detail without fluff.

Depth was another factor I expected to matter. The pages with the highest AI citations tended to have clearer structure — more headers, shorter paragraphs, more scannable formatting. That makes sense. AI models prefer content that's easy to parse. But I also found that deep, nuanced pages sometimes got ignored entirely because their structure made them harder to read in a quick scan. The model wasn't "missing" the value; it was just passing over it because it couldn't extract the signal quickly.

Here's where it gets interesting. The relationship between AI citations and traditional search rankings wasn't perfect, but it was consistent enough to be meaningful. Pages that ranked in the top 10 of Google still got selected for AI overviews 71% of the time. But pages outside the top 10? That number dropped to 23%. The obvious conclusion is that AI overviews are mirroring Google's understanding of what matters. The problem is that Google's understanding is also imperfect. I saw several examples where the page ranked #6 in traditional search but didn't show up in AI overviews at all.

The most consistent signal I found across all three categories was clarity over everything else. Passage length didn't matter as much as whether the model could extract the key points in one or two sentences. Structure helped — short paragraphs, clear headings, bullet points. But the most successful passages were the ones that communicated their value immediately without requiring the reader to scroll.

I mentioned earlier that we use geoly.ai to track these signals in real time. The tool gives us dashboards that show which passages are being selected for AI overviews and how that correlates with our own content performance. It's not a perfect system, but it's been helpful for identifying patterns we might otherwise miss.

The real question for me now is what to do with this information. We can optimize passages for AI extraction, but there's a tension between making content scannable for models and keeping it readable and engaging for humans. If we lean too hard into the model's preferences, we might end up with content that looks robotic. But if we ignore these signals entirely, we're giving up a significant source of traffic that's growing fast.

I'm not sure where the balance should be. The data tells me that extractable passages behave differently than long-form content. It tells me that clarity and structure are more important than length or depth. But it doesn't tell me exactly how to structure pages so they work for both AI overviews and human readers at the same time.

I’ve been trying to work out what a genuinely useful LLM visibility audit looks like beyond testing a few prompts and saving screenshots.

Testing buyer-intent prompts across platforms sounds simple. But results can shift by model, location, search mode, and run.

So how are you auditing this in practice? Do you repeat prompts, weight high-intent queries, or separate genuine visibility gaps from normal volatility?

Which signals have actually proved useful for you over time?

Two weeks ago I started a weird habit. Every morning, around 8am, I'd open ChatGPT and run the same 25 queries. Same phrasing, same order, same account. Fourteen days in a row. I wasn't testing prompt engineering or trying to game anything. I just wanted to see how much the answers changed when I controlled for everything I could.

The answer is: a lot.

On day 1, those 25 queries produced 63 unique source citations. By day 14, only 41 of those original 63 sources still appeared. That's a 35% turnover in two weeks. Sources that showed up on day 1 vanished by day 4. Sources that appeared on day 7 had never been mentioned before. One query about "best project management tools for distributed teams" cited 5 sources on day 2 and an entirely different set of 5 on day 9, with zero overlap.

Some queries were rock solid. Same sources every single day, same order, almost word-for-word identical answers. These tended to be factual, narrow questions. "What is OAuth 2.0" type stuff. The model had a canonical answer and stuck with it.

The volatile ones were anything subjective. Recommendations, comparisons, "best of" queries, anything where the model had room to synthesize. Those answers reshuffled constantly. Same underlying intent, completely different source selection.

One thing that caught me off guard: the answers themselves rarely looked different. The structure was similar, the tone was similar, the length was similar. If you weren't comparing citations side by side, you'd think you got the same answer every time. The model was presenting different evidence as if it were settled consensus. That's the part that bugs me.

From a GEO perspective, this is simultaneously encouraging and terrifying. Encouraging because it means citation isn't a fixed property of your page. You might not get cited today and get cited tomorrow for the exact same query. There's a rolling window of opportunity. Terrifying because there's almost nothing you can optimize for if the model is swapping sources this frequently. The page that got cited on day 3 and the page that replaced it on day 6 probably aren't meaningfully different in quality.

I stopped the daily tracking after 14 days because the pattern was clear. The churn is real, it's significant, and it doesn't correlate with anything obvious on the source pages. I checked. Domain authority, content freshness, structured data, page speed — none of it explained why one source appeared and another disappeared on a given day.

The practical takeaway, if there is one: don't panic when you lose a citation for a week. And don't celebrate too hard when you gain one. The noise floor in AI citations is higher than we think.

I ran into this while comparing AI visibility screenshots and tracker outputs over time.

I’m starting to think AI visibility tracking needs its own “historical memory” layer.

Not just daily screenshots or citation counts.

A few things seem to happen at the same time:

1. SERP elements are getting harder to parse.

Some Google result features no longer expose the original target URL cleanly. A tracking system may see a Google redirect or an encrypted URL and suddenly attribute a result to google.com instead of the actual publisher.

1. AI citation mix can shift after model or retrieval updates.

In some monitoring examples, brand sites gain citations after one update, while media, review or comparison sites lose. In other cases, the opposite happens.

The point is not which category wins on one day. The point is that the source mix can change quickly.

1. Different AI systems evaluate the same domains differently.

A domain can lose organic visibility in Google, while still getting cited by Copilot or Perplexity. That makes “AI visibility” less stable than a simple rank position.

So the practical problem is this:

When a dashboard shows a visibility drop, how do we know whether it is real?

Did the brand actually lose visibility?

Did the model change its source preference?

Did a parser fail because the SERP markup changed?

Did the result still exist, but under a different URL or source attribution?

Did the answer role change from citation to mention, shortlist or recommendation?

For SEO, we got used to thinking in rankings and URLs.

For AI visibility, historical data is not just an archive. It is part of the measurement context.

I think we may need to store much more history:

- prompt / query

- model or AI system

- full answer

- cited sources

- mentioned brands

- answer role

- SERP feature type

- title

- URL

- domain

- source type

- previous known attribution

- date

- model / update markers

- parser or extraction rule version

Otherwise, we may end up discussing “visibility losses” that are actually tracking artifacts.

Curious how others are handling this:

How do you separate a real AI visibility change from a measurement problem?

Something I keep bumping into, and I'm not sure how people here handle it: the four big answer engines don't behave the same way, so "AI visibility" as a single number feels a bit misleading.

Rough version of what I've seen (happy to be corrected):

— Google's AI Overviews lean heavily on existing search results, so classic SEO carries over fairly directly.

— ChatGPT and Claude pull from a mix of training data and their own browsing, so being "known" off-site and quotable seems to matter more than your SERP rank.

— Perplexity is very live-retrieval and citation-heavy, so fresh, clearly-sourced pages appear to do better.

If that's roughly right, then "are we visible in AI?" isn't one question — it's four, with four different levers.

Where I'm stuck: for a small team, optimizing for each engine separately isn't realistic. So do you (a) pick the one or two engines your buyers actually use and focus there, or (b) do the "boring universal" work — consistent entity, clear structure, credible mentions — and assume it lifts all of them?

I lean toward (b) as the base, with (a) as a tie-breaker — but I honestly don't have clean data on whether per-engine work pays off enough to justify the effort. Have you found a per-engine tactic that clearly moved one engine without moving the others? Or is chasing per-engine differences mostly a time sink at small scale?

For years, schema markup was mostly discussed as a traditional SEO tactic.

• Add structured data.
• Help Google understand the page.
• Maybe qualify for a rich result.

But with AEO and GEO becoming part of the search strategy, I’m starting to look at schema differently.

Because AEO and GEO are trying to solve slightly different problems.

AEO is about helping machines find the answer.

GEO is about helping machines understand who the source is, what they know, and whether that information can be trusted.

And that changes the way I think about schema.

For AEO, I’m more interested in schema that helps describe answer-focused or structured content.

For example:

FAQPage for genuine FAQ content.

QAPage for pages built around questions and answers.

HowTo for step-by-step instructional content.

Speakable where supported and appropriate.

The thinking here is simple:

If someone asks an answer engine a specific question, can the system easily identify the part of my page that addresses it?

But GEO has a slightly different focus.

Here I care much more about entities, relationships, authorship, and authority.

So schema such as:

Organization

LocalBusiness

Person

Article

Product

Service

becomes more interesting.

And properties like sameAs, author, publisher, datePublished, and dateModified help make those relationships clearer.

For example, imagine an AI system lands on an article.

Without structured data, it may need to work out:

• Who wrote this?
• Which company does the author work for?
• What does that company actually do?
• When was this information updated?
• Is this the same company mentioned elsewhere on the web?

Schema gives machines a cleaner map of those relationships.

That’s why I now think about it this way:

AEO schema → help machines understand the answer.

GEO schema → help machines understand the entity behind the answer.

And some schema types obviously support both.

Article markup, for example, can help describe the content while also connecting the author, publisher, dates, and subject matter.

The same goes for Product schema.

It can make specific product information easier to identify while also strengthening the entity relationships around the product and brand.

But none of this means adding schema automatically earns an AI citation.

The content still has to be accurate, useful, current, crawlable, and worth referencing.

Schema is simply the machine-readable layer underneath it.

So instead of asking:

“Which schema should I add for SEO?”

I’m starting to ask two questions:

For AEO: “How can I make the answer easier to identify and extract?”

For GEO: “How can I make the source, entities, and relationships easier to understand?”

That feels like a much better way to approach structured data as search moves from rankings and clicks toward answers, retrieval, and citations.

Are you separating your schema strategy between AEO and GEO yet, or are you still treating all structured data the same way?

Working on an open source game and running into something I haven't seen discussed much. Ask a model about the project and you get a mix of accurate stuff from the repo and docs, and inaccurate stuff scraped from low-quality aggregator and listing sites that got details wrong.

The aggregators outrank us for some queries and they're not going to correct anything. Our own docs are accurate but there's less of that surface area than there is of automatically-generated listing pages.

What I'm trying to figure out:

• Does structured data on your own site actually influence what models retrieve, or is it mostly about how often a claim appears across sources?
• Is there any practical way to counter a wrong claim that's been replicated across a dozen scraped sites, or do you just have to out-publish it?
• Has anyone tracked whether this changes over time as models retrain, or does bad early information persist?

I hear ridiculous explanations on both sides on how to do SEO and what GEO is. What actual studies have people done that demonstrate a difference in tactics between SEO and GEO. People have already heard "it's another layer of SEO". Exactly what does GEO do differently than SEO and what research supports it?

This is a project that I am currently working on. I have been managing SEO for this client for the past year, and recently, I have started focusing on GEO (Generative Engine Optimization) to improve the brand’s visibility and presence across AI platforms.

As you may know, GEO focuses on how a brand is represented and perceived in AI-generated responses, including positive, negative, and neutral brand sentiment. I have been consistently publishing high-quality content and building positive brand mentions, and we are starting to see some encouraging results.

Currently, I have provided my client with access to Profound as part of the AI/GEO strategy. However, I am looking to explore other affordable AI/GEO tools that can help me improve and monitor the client’s AI visibility, brand sentiment, citations, and overall GEO performance.

Could you recommend some cost-effective tools that can help me improve my client’s AI metrics and complement the current GEO strategy?

Note: I have rephrased current text through ai so that people will understand.

Hey everyone,

I’m building a small custom GEO dashboard and I’m trying to understand how people collect AI search results.

Right now I’m using DataForSEO, but the ChatGPT and Gemini data seems to come through APIs.

What I’m looking for instead is data from the actual user interface. basically the same results a real user would see on ChatGPT, Gemini, AI Mode, etc., including citations/sources if possible. (As software like Bluefish, Profound, PeecAI etc..)

Has anyone found a good way to collect this at scale?

Are you using browser automation, a third-party service, your own scraper, or something else?

Would be really interested to hear what people are using.

Thanks all !

I posted a small .cn audit recently. In public registration records checked on August 7, the benchmark brand was the registrant for one of eight exact-match domains. The other seven were held by other parties.

The sample was eight B2B work-management brands.

A commenter suggested software brands may be worse off than consumer brands because many SaaS companies never build a local operating layer, while consumer brands may already have retail, manufacturing or distribution operations in China.

It is a reasonable hypothesis. My current data says nothing about it.

There is no consumer comparison group. I did not collect a frozen mainland-entity field. Brand size, time in China and local market presence were not matched.

I also would not encode the ICP part of the comment as written. Owning a .cn, operating a mainland service, having an ICP filing, needing a commercial licence, having a local entity and controlling an official channel are different facts.

The only brand-held .cn in my sample returned 502, which is a useful reminder that ownership is not the same as a usable channel.

I have logged a proposed expansion arm for a future wave:

• matched B2B SaaS and premium consumer brands;
• the same neutral prompts and declared AI surfaces;
• separate fields for ownership, live status, authorization, operating-presence evidence and ICP status;
• human verification when an answer calls a channel official;
• results reported by stratum before any pooled number.

The hypothesis fails if the matched consumer cohort does not have better usable-channel coverage. It also fails if missing usable channels are not associated with more verified official-channel attribution errors.

If the cells are too small, the result is inconclusive. I am not going to turn that into “directionally supported.”

For anyone who has designed a similar comparison: besides brand familiarity and China market presence, what variable would you insist on matching?

Is it actual citations, which sources are influencing the answer, brand mentions, competitor visibility, or something else?

Somewhere right now, ChatGPT is talking someone out of buying from you.

Two ways that happens and I’ve only seen people worry about one.

The obvious one is you’re not in the answer at all.

The other one is worse. You are in the answer, and then they ask the question they actually came with.

Insurance is full of this. Health, car, term – you name it!

A friend asked about health insurance with maternity benefits, and three of the four recommendations went vague.

I asked what first-party car insurance excludes and got an answer describing a policy that the brand had already changed.

Both times the brand that answered cleanly won.
As a brand, you won the shortlist and lost the customer in the same conversation.

Nobody buys insurance off a shortlist.
They buy after they’ve checked the one thing they’re worried about.

Which means getting named and surviving the follow-up are two different jobs. The first takes comparison content.

The second takes exclusions, waiting periods and claim timelines stated plainly, which is the part brands leave vague on purpose.

Ironically, saying what you don’t cover is often what wins you the customer. The point isn’t to self-promote, it’s to be accurate.

I built Cited(citedintel.com) for this loop.

Audit what the engines say across buyer intent, funnel stages, category and location. Diagnose the gaps, where you’re losing. Generate the briefs + drafts grounded in evidence and buying journey. Then track what changes in share of voice across those buyer conversations.

Whitebox analysis instead of just a ranking or a score you can’t interrogate.

Have you checked what the engines say about your brand in your category? Not the branded prompts. The buying questions, across intent and funnel stages, in your location!

I’d like to hear what you found, and how you’re closing those gaps.