r/GEO_optimization

SEOs have been taught for decades that URLs should be descriptive. A URL such as /wordpress-performance-optimization/ is considered better than something meaningless like /x7ab31/.

But that assumes the crawler actually needs to fetch the page before deciding what it is about.

I've been watching AI crawler behavior more closely, particularly GPTBot and ClaudeBot, and something made me question that assumption.

If a crawler can infer enough about the likely content from the URL, link context and surrounding semantics, it can also decide that the page isn't worth fetching - without ever seeing the actual content.

So I tested the opposite approach.

I exposed alternative Markdown representations of existing content through completely opaque URLs. The URLs contained no topic, keyword or other clue about what was behind them.

Both GPTBot and ClaudeBot discovered and fetched all of them.

This obviously doesn't prove that the content is used for training, citations or AI answers. It only proves something much earlier in the chain: the content was actually retrieved.

And that made me wonder whether GEO is currently starting too late.

Most GEO advice focuses on optimizing content for AI systems after discovery: structure, entities, concise answers, citations, schema, etc.

But what if the first optimization layer should be:

Make sure the AI crawler actually sees the content in the first place.

In that context, a descriptive URL may not always be an advantage. It may also give a crawler enough information to reject the content before fetching it.

Maybe AI discovery needs to be treated as its own optimization layer, separate from traditional SEO.

Has anyone else observed crawler selection happening before the actual page fetch?

Running a small SaaS (domain investing niche), launched about 3 months ago. Got an AI-assisted pipeline that writes and distributes blog content across a few channels, submitted to GSC/Bing, posting regularly — but I think the pipeline is missing a lot of actual SEO/GEO fundamentals since I built it more for output volume than optimization.

Looking for practical stuff I can bake directly into the pipeline/prompts, not general theory. Specifically curious about:

• Any prompt structures or content templates you use to make articles more "citeable" by AI engines (ChatGPT, Perplexity, AI Overviews)?
• Specific on-page/technical things (schema markup, FAQ structuring, heading patterns, internal linking rules) that you've actually seen make a measurable difference, that I could turn into a checklist or template?
• For a new site with no authority — any tactics for getting early backlinks/mentions that could be systematized rather than one-off?
• If you've built or use an AI content pipeline yourself, what do you have it check/enforce before publishing?

Basically trying to turn whatever works into repeatable rules I can apply automatically going forward, instead of guessing article by article. Any concrete tips, prompts, or checklists you're willing to share would be huge.

https://preview.redd.it/f8z0ek30z8kh1.png?width=2184&format=png&auto=webp&s=281db360d8ea95d7b0d32293183cee6a9b9dd950

Promptwatch data making the rounds shows Reddit's share of ChatGPT Search citations fell off a cliff after OpenAI's Aug 8 query-fanout change, from a steady ~3.8% down to ~0.5% by Aug 14.

So the actual lesson isn't Reddit is dead for GEO. It's that citation share is the single most volatile, platform-controlled metric in this entire field, and building a visibility strategy on top of a number that OpenAI can swing 80% with one backend change is the real mistake.

The durable play imo should never be "get cited this week." It should be building a brand presence that survives the swings, making it much different and much less measurable.

Google recently said that having an llms.txt file won’t help your Google Search rankings.

But I still see it near the top of a lot of AEO/GEO checklists:

• Add llms.txt
• Make your site AI-readable
• Submit content for LLM crawlers
• etc.

I'm starting to wonder if we're optimizing for things that are easy to check rather than things that actually influence whether an AI recommends a brand.

Has anyone here actually seen a measurable difference after implementing llms.txt?

I know adding llm.txt is advisable to help LLM pick the website, but is it necessary? Is there any relevant data?

I wasn't looking for this pattern.

I was running a small side project comparing how AI models handle different types of "authority" signals. Nothing fancy. 50 queries across niches like B2B marketing automation, enterprise security compliance, and product-led growth strategy. For each query, I checked which sources ChatGPT, Perplexity, and Gemini cited, then categorized each source as either a personal brand (LinkedIn, personal blog, individual's byline page) or a company domain (official site, resource center, press room).

The assumption going in was that company domains would dominate. They have more content, bigger teams, better structured data, larger link profiles. Everything we're told matters for GEO.

The numbers went the other way. Individual contributors got cited 2.4 times more often than brand domains for the exact same queries. Not in every single case, but consistently enough that it showed up across all three models and most query categories.

I started digging into why. A few things stood out.

Personal profiles tended to have clearer point-of-view. When you read a company's "about us" page or resource article, the voice is usually neutral, committee-written, designed to offend nobody. Safe. Individual contributors, especially ones who've built followings through writing or speaking, tend to have opinions. They take positions. They say things like "in my experience" or "here's what I got wrong." That specificity seems to register differently when a model is selecting sources for an expertise query.

Another thing: personal profiles often consolidate expertise signals in one place. A well-maintained LinkedIn profile or personal site might list credentials, publications, speaking engagements, and client work all on a single page. Company domains spread that same information across dozens of pages — team pages, press releases, blog author bios, case study footers. The signal is there but fragmented.

The third observation is messier and I'm less sure about it. Individual contributors' content tends to get shared and referenced in forums, podcasts, and social discussions more than corporate content does. Those secondary mentions might be creating a feedback loop where the model sees the person's name in multiple contexts and builds stronger entity association. Pure speculation on my part, but the correlation is there.

What I can't explain is whether this is actually about quality or about something structural in how models evaluate sources. Maybe individual contributors really do produce better expertise content on average. Maybe models have a bias toward named individuals over faceless organizations. Maybe company domains are being penalized for sounding too much like marketing.

I don't have a clean theory yet. The sample size is modest and the queries skew toward consulting-style topics where personal brands naturally thrive. Would love to see if anyone else has looked at this split, or if you're seeing the same thing in your niches.

Still figuring out if this is a temporary blip or a structural shift in how AI evaluates authority. Either way, it's making me rethink what "entity optimization" actually means when the entity is a person, not a logo.

Rather than trying to spread yourself thin across every engine, you should be choosing the ones where your buyers are and sticking to those.

A few things I'd look at before picking.

Start with, where are your buyers? Enterprise B2B skews ChatGPT, researchers and technical buyers skew Perplexity and consumer products lean Google AI Overviews. 

Then look at where your existing content already has traction. ChatGPT leans institutional (G2, established publications) and Perplexity like UGC (Reddit, YouTube). Match the engine to where you already have presence.

The last question is where's the biggest competitor gap? The engine where competitors are weakest is the opportunity worth going after first.

Pick one engine, get real traction there, then expand.

Which engine are you prioritizing, and what drove that call?

I ran a scan recently that came back at zero. No presence, and a long list of competitors appearing in answers where the brand did not.

Read at face value, that is a catastrophic result.

It was not a visibility result at all.

The organization does capacity building and accelerator programs. The competitor list that came back was full of large, well known design and branding studios. Different industry, different buyers, different everything. It has never competed with any of them and never will.

One word in its name reads as a different category. The engines had filed it there, so the scan measured its performance in a market it has never entered.

The number was real. The measurement was pointed at the wrong thing.

What makes this uncomfortable is that nothing in the output flags it. A zero from being invisible in your actual category and a zero from being scored inside someone else's category look identical. Same number, same competitor count, same red panel. Only reading the competitor list carefully catches it, and the entire appeal of a score is that you do not have to read anything carefully.

I think this argues for an ordering that most tracking has backwards.

Before asking whether the engines mention you, ask whether they know what you are. Run the plain identity question across ChatGPT, Claude, Gemini and Perplexity. Who is X, what do they do, who is it for. Then compare the four answers against each other, and separately against how the company describes itself.

Three different failures show up there and they need different fixes.

The engines disagree with each other on facts that have one right answer. Location, ownership, what they sell. That is entity confusion, and nothing downstream is trustworthy until it is cleared.

The engines agree with each other and disagree with the company about what category it is in. That is what I hit. Positioning is not disambiguating the brand from an adjacent industry, and no amount of evidence building helps while the evidence is being filed in the wrong drawer.

The engines agree with each other and with the company. Only then is a low score a real visibility finding worth acting on.

One of those three justifies a visibility programme. It is also the only one of the three most tools are built to detect.

The check costs nothing and takes about fifteen minutes.

Two things I am unsure about.

Whether the misfiling comes from the name itself, or from thin evidence letting the name dominate. A well evidenced brand with an ambiguous name presumably survives it, which would make this a symptom of evidence thinness rather than a separate problem.

And whether it is correctable from the brand's own properties at all, or whether it takes independent sources describing it in the right category before the filing moves. If it is the second, the fix is much slower than a positioning rewrite and most advice on this is wrong.

Has anyone watched a category misclassification actually correct, and what moved it?

We wanted to understand what happens to brand recommendations when the model changes but the questions stay the same.

We gave GPT-5.4, GPT-5.5, and GPT-5.6 Sol the same panel of 270 category questions across six industries.

A few findings from GPT-5.5 to GPT-5.6 Sol:

• 70% of matched answers became shorter.
• Median answer length fell from 224.5 to 141 words.
• Median named brands only moved from 21 to 20.
• Explicit caveats fell from 40.7% to 20.4%.
• Decision-framework language fell from 33.3% to 13%.
• Retail shortlists narrowed from 20 to 13 brands, while Travel widened from 24 to 27.

The interesting part is that model updates don't create one universal change in brand visibility. They can compress explanations, remove caveats, ask for more context, or handle individual markets differently.

The report includes the methodology, industry breakdowns, exact model values, and links to the underlying model answers:

https://app.nyman.media/insights/ai-visibility

I’d be interested in feedback on the findings and also the methodology. What categories, models, or question types would you test next?

Google confirmed this month that Gemini now powers every search query by default, not just the AI Mode tab people were opting into. Blue links are still there, but they're not really the interface anymore — you ask, Google answers, you follow up conversationally, and the results page turns into something you scroll past. Zero-click rates in AI Mode are sitting close to 90%+ now, publishers have lost 80-90% of their organic traffic over the last year and a half as this rolled out, and AI Mode query volume has been more than doubling every quarter since launch.

I keep coming back to how quickly the ground shifted under an assumption nobody really questioned. The GEO framing I've seen so far — including in how I was thinking about it — has mostly been "track your citations and sentiment across ChatGPT, Perplexity, etc., alongside your regular SEO." That only works if Google search is still mostly Google search — a stable channel sitting next to the AI stuff, something you keep doing what you'd always done for while treating GEO as the new thing on top. That assumption doesn't hold anymore, and I don't think it's just a tooling gap. It's every business that built its growth engine on the old rules — content teams optimizing for rankings that don't lead anywhere, budgets still weighted toward a channel that no longer sends traffic the way it used to, whole functions built around a feedback loop (rank → traffic → revenue) that's quietly stopped working for a majority of queries. I don't think most orgs have sat with what that actually means yet.

The tools built around "track mentions across four or five AI platforms" now have to treat Google as one of those platforms too, except at a scale nothing else comes close to. And the gap I was asking about in my last post — going from "here's your citation score" to "here's what to do and proof it worked" — just got more expensive to leave open, because this isn't a side channel anymore, it's the default way people find anything. I don't think most GEO tooling, including stuff I've used myself, was built for that. Curious if others building or using these tools — or running marketing/growth at their own companies — are seeing the same thing, or if I'm reading too much into one announcement.

I firmly believe this is the best robots.txt for most WordPress sites. Prove me wrong:

User-Agent: *
Disallow: /wp-admin/
Disallow: /search/
Disallow: /feed/
Allow: /wp-admin/admin-ajax.php

User-agent: GPTBot
Allow: /

User-agent: OAI-SearchBot
Allow: /

User-agent: ClaudeBot
Allow: /

User-agent: PerplexityBot
Allow: /

User-agent: CCBot
Allow: /

User-agent: Google-Extended
Allow: /

Sitemap: https://www.example.com/sitemap_index.xml

Look, I get it. Seeing your brand pop up in a ChatGPT or Perplexity response gives you that dopamine hit. But if you are treating these AI mention dashboards like a replacement for Google Rank Trackers or smthn, you are about to waste a massive budget.

Everyone is obsessed with "Share of Model," but nobody wants to admit the ground has shifted. We aren't tracking rankings anymore, we are tracking perception - and perception is a slot machine lol.

Here are the 7 hard pills you need to swallow before you buy that expensive GEO tool:

1. It’s probabilistic, not static.

If you prompt the same AI twice, it might cite you once and ignore you the next time. That isn't a bug, it’s the architecture. If the software tries to assign you a "fixed position" in the model, they are lying to you. Stop obsessing over the "rank." Watch the consistency.

2. A citation is NOT visibility.

An AI answer might generate 3,000 words and drop your name in paragraph 6. Guess what? Nobody scrolled to paragraph 6. If you are counting raw mentions without checking where in the response you appear (the "retrieval context"), you are counting noise.

3. Hallucinations are ruining your data.

This is the scary one. An LLM can hallucinate your brand name into an answer without ever retrieving your actual webpage. If you don’t manually validate these mentions against source-level evidence, your dashboard is just showing you fanfiction.

4. "Share of Model" is BS.

Every single tool defines a "mention" differently. One counts it if your name is anywhere in the prompt, another only counts it if you are used as a primary source. There is no industry standard. Ask for the methodology before you buy, or you are comparing apples to oranges.

5. Geo-fragmentation is real.

If your CMO shows a screenshot of a ChatGPT answer from New York to prove we are "winning," laugh at them. Run the exact same prompt in London, Tokyo, or on a logged-out browser. The outputs are wildly different. One screenshot proves nothing.

6. The engines are not the same (!).

Stop bundling AI traffic into one bucket.

- ChatGPT loves community/forum sources (Reddit, Quora).

- Perplexity rewards the freshest, newest content.

- Gemini tends to favor big, "official" institutional data.

If you aren't segmenting your strategy by engine, you are shooting in the dark.

7. If you aren't tying this to revenue, stop now.

Don't retire your legacy analytics. If you get a 100% AI mention rate but your organic revenue is flat, you are just famous among robots. Blend the AI data with your actual Webflow/GA4 revenue KPIs. Prove that the mention actually converts.

The Bottom Line:

AI tracking isn't Rank Tracker 2.0. It’s a probabilistic, fragile, data-crunching exercise. If your vendor sells it as a simple "visibility score," they are selling snake oil.

TL;DR: Don't buy the dashboard for the number. Buy it for the context. And if they don't let you drill down to the specific source of the citation, save your cash.

Well, I'm done for now, hope it was useful huh

I've spent the last six months tracking every possible signal we could extract from AI answers. Source quality, citation order, answer length, whether the model included a summary paragraph, how many entities it mentioned — you name it, I've measured it. But every time I cross-reference these signals with our own evaluation of answer quality, nothing sticks.

Three features stood out as consistent measurements, and all three turned out useless.

Answer length was the first one. Longer answers tend to include more detail, more nuance, more context. That feels right, so I expected longer answers to correlate with better quality. I was wrong. Across different categories — technical questions, product recommendations, research summaries — the relationship was random. Some of the best answers we found were 150 words. Some of the worst were 400. Length wasn't a signal. It was just noise.

Then there's the summary paragraph. A lot of AI models now include a brief "In summary" or "Here's what I found" section before diving into details. It looks authoritative. It looks complete. It feels like a feature. But when we compared summaries to overall answer quality, the correlation was weak. Some of the best answers had no summary. Some of the worst had long, detailed summaries that didn't actually improve clarity.

Entity density is the same story. More people, organizations, locations, specific products mentioned — it feels comprehensive. But again, no signal. We saw both the most and least detailed answers with high entity counts. The entities weren't telling us anything useful about whether the answer was good or bad.

I keep coming back to why we measure these things. Answer length looks like a proxy for depth. Summary paragraphs look like a proxy for completeness. Entity density looks like a proxy for breadth. But if they don't actually correlate with quality, they're not proxies. They're just things we can count.

This is the uncomfortable part. We optimize for what we can measure. When we build dashboards and reports, we highlight answer length and summary presence and entity counts because they're visual. They fill space on the screen. They look important. They don't require interpretation — we just show the number.

The quality evaluation requires human judgment. It requires context. It requires deciding what "good" actually means in each case. That's messy. So we optimize for the clean metrics instead.

I don't have a solution here. I just know that our measurement infrastructure is better at tracking signals than predicting quality. The dashboards look impressive. The charts are clean. But when it comes to actually understanding what makes AI answers valuable, the numbers don't tell us much.

Maybe that's the point. Maybe the features that actually correlate with quality are the ones we can't easily measure. Maybe good answers are qualitative, not quantitative. Maybe we're building the wrong infrastructure entirely.

I'm not sure what to do with this realization. I just keep looking at the dashboards and expecting the numbers to make sense, and they don't.

I keep seeing more tools that promise to automate a big part of SEO: keyword research, content briefs, article generation, internal linking, publishing, etc.

Curious to hear from people who have actually paid for and used them. Like babylovegrowth, sorank, autoseo, blogseo stuff like that

Which tools have you tried?

What did they genuinely do well?

What felt disappointing or overhyped?

Did the content actually rank, or did it mostly save time?

And was there anything you expected the tool to handle that it just didn't?

Especially interested in experiences with tools like BlogSEO and similar platforms, but open to anything.

Would love actual user experiences rather than affiliate/review-site recommendations.

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?

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 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?

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 kept running into a weird problem while checking ecommerce product pages. The information was technically there, but an agent still could not use it with much confidence.

https://preview.redd.it/z996649qbmjh1.png?width=1254&format=png&auto=webp&s=fa249ae31bd1baa567e3f490c62f274dbad4f65e

After a few audits, I started checking every product fact in three ways:

1. Can a customer see it?
2. Is it exposed in machine-readable product data?
3. Does it agree with the other sources?

That catches things a normal schema check misses:

- ratings shown on the page, but no AggregateRating

- €19 on the product page and €20 in the feed

- InStock in JSON-LD while inventory says sold out

- a 30-day return policy on the site, but a final-sale rule on the product

- material shown in an image and nowhere else

The schema can be valid while the data is stale. The feed can be right while the page is wrong. Both can exist and still leave the agent guessing.

A single score hides too much. The useful part is the audit trail:

source -> fact -> conflict -> fix

For AI shopping, more copy is not the answer to this problem. The page, schema, feed, catalog, inventory and policies have to agree.

I am still working out how to prioritize these conflicts. Right now I treat price and stock as blockers, missing attributes as discovery issues, and policy conflicts as trust issues.

How would you rank them?

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.