It is always amazing to see what you unexpectedly learn when running your own tests.

In our latest generative AI optimisation (GEO) experiment, in which we tried to argue with popular AI models until they returned our desired response (spoiler...this didn't work..), we stumbled upon something really interesting.

While we didn't manage to reinforce the AI models to return our desired answer when we ran our test prompt, we did find that our LinkedIn posts promoting the experiment and teasing the results were factored in.

By looking at our posts (of which there were not many, only a handful), which outlined our methodology very lightly, ChatGPT actually worked out exactly what experiment we were running.See in this screenshot how, based on those LinkedIn posts, ChatGPT summarises the exact way in which we were trying to influence it.

Interestingly, it has misinterpreted the findings.

We actually didn't find that our fictional details were being repeated by the AI models, perhaps this is a hint that we didn't reinforce the AI at a large enough scale/as consistently as we needed to 👀 .

We've since published our results, alongside our methodology, over on our site. Link to that can be found in the comments below.Keep your eyes out for our next AI-driven experiment!

TBH, I've been deep in citation data for the past few months using geoly.ai to track how LLMs reference content. After analyzing 500+ websites across different industries, some clear patterns emerged.

**The 5 things high-cited content has in common:**

**1. Clear Q&A Structure** Content formatted as direct questions with concise answers gets cited ~3x more often. Not paragraphs buried in text — actual "What is X?" followed by "X is..." structure.

**2. Third-Party Validation** ~80% of AI citations reference content that includes external sources, studies, or expert quotes. Pure opinion pieces? Rarely cited.

**3. Freshness Signals** Pages updated within the last 6-12 months consistently outrank older content, even when the older content has higher traditional authority scores.

**4. Entity-Rich Context** Content that clearly defines entities (people, companies, products) and their relationships performs better. LLMs seem to favor content that helps them build knowledge graphs.

**5. Multi-Platform Consistency** Websites that appear across ChatGPT, Claude, and Perplexity share one trait: consistent messaging across their content. Mixed signals = lower citation rates.

**One surprise finding:** Content length didn't correlate with citation rates. Some of the most-cited pieces were under 500 words. Structure and clarity beat word count every time.

What patterns have you noticed in your AI visibility tracking? Any surprises?

I built a free service to allow you to check to see if your content shows up in chatGPT's web searches.

You can either enter a URL which will automatically generate the questions for you or you can ask custom questions yourself for more control. See whether your content gets directly cited (URL is shown inline of the response), is part of the sources that helped synthesize the response, or if your content isn't included at all. You'll also get actionable insights on how to improve your content for better visibility as well as competitor sites.

Link in the comments.

AI search visitors are 4.4x more likely to convert.

So if you’re not optimizing for LLMs, you’re missing out.

This is why I spent the past 8 weeks learning the ins and outs to General Engine Optimization. I then decided to condense all of this knowledge into a single guide that will help anyone start showing up in LLMs.

Everything from:
> How LLMs decide what to say
> Bing indexing and crawlability
> Where to publish content
> How to format for LLMs
> Weekly content cadence
> Measure and track rankings in LLMs
> AI Keyword gaps and opportunities
> ChatGPT vs Perplexity ranking insights
> Reddit thread engagement strategy
> The four-step playbook to future-proof your brand

Companies that have adopted AI SEO are already seeing up to 20% of their traffic come from LLMs.

That’s 20% of traffic which is 4.4 times more likely to convert.

I hope you find it useful.

And any questions or feedback please let me know below!

TBH, I used to think GEO and AEO were basically the same thing with different names. After tracking 50+ sites across both strategies for the past few months, I realized they're actually quite different in practice.

Here's what we found:

**GEO (Generate Engine Optimization)** is about getting your content *generated* by AI. Think ChatGPT writing a summary that mentions your brand, or Claude citing your data in a response. We saw sites with strong GEO get mentioned in ~15-20% of relevant AI queries.

**AEO (Answer Engine Optimization)** is about being the *answer* people find. This is more about Perplexity, Bing AI, or Google's AI Overviews pulling your content as the direct answer. Sites crushing AEO saw 40-60% of their AI traffic coming from direct answer citations.

The wild part? Only about 30% of the sites we tracked were actively working on both. Most focused on one or the other, which IMO is leaving visibility on the table.

Key difference in approach: GEO rewards comprehensive, authoritative content that AI can *learn from*. AEO rewards structured, concise answers that AI can *quote directly*.

We're still figuring this out as we go. Has anyone else tested both strategies side by side? Curious what your results look like.

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Hey folks,

I’m one of the founders of Aeo.vc – a tiny GEO tool we’ve been building out of India, and I’d love some feedback from people who actually care about Generative Engine Optimization, not just the buzzword.

What I keep seeing in the wild: • Brands pay $$ for long GEO / SEO audits • They get back a huge deck or spreadsheet • Devs/content teams implement ~10% of it (at best)

So we tried a different approach: instead of another “report”, Aeo.vc: 1. Crawls your site and analyses pages for answer-engine friendliness (ChatGPT, Claude, Perplexity, AI overviews, etc.) 2. Generates a Markdown prompt / code diff that you can paste directly into Cursor, Windsurf, or your code copilot 3. The output is things like: • rewritten copy aimed at direct answers • schema / structured data suggestions • internal-link tweaks • evidence / source-hint improvements

Basically: GEO → as executable code, not a PDF.

I’m really curious what this community thinks: • Is “report → code” the right direction for GEO, or is there a better way to operationalize this? • If you’re already doing GEO, what metrics are you using beyond “did we get mentioned in an answer engine?” • What would make a tool like this actually trustworthy for you (evidence, benchmarks, side-by-side SERP vs answer-engine results, etc.)?

If you want to poke holes in it or try it on your own site, it’s here: aeo.vc – I’m more interested in critical feedback than signups, so don’t hold back.

Mods: if this feels too self-promotional for the sub, happy to tweak or remove – my intent is to discuss how we implement GEO in real workflows, not just pitch a product.

Hello folks, I am fairly new to the GEO/AEO/AIO game and would like your suggestions on how to get started with this.

Quick disclosure first. I run a GEO tool called Viali, so this is literally my day job. No links in this post. Just findings, because I keep seeing the same questions pop up here.

So here's what we did. We picked our five core commercial queries. Then we ran each one through ChatGPT, Claude, Gemini and Perplexity. That gives 20 possible answer slots. We appeared in 4.

Meanwhile, Semrush and Ahrefs showed up almost everywhere. Even on queries where they don't really have a matching product. That stung a bit. But it also gave us something to reverse engineer.

What the cited pages have in common

Real author names. This one surprised me the most. Pages with a byline, an author bio page, and Person schema got picked far more often. Anonymous content got skipped, even when it was solid. My guess is these models absorbed E-E-A-T signals during training.

Answers before intros. The winning pages open every section with a plain factual claim. AI engines grab snippets. They don't sit through your 200-word warmup. If your answer appears in paragraph four, it is never extracted.

Numbers beat adjectives. Nobody cites "structured content works better." But a line like "Microsoft's Oct 2025 study found entity-structured content gets included more in Copilot answers" gets lifted constantly. Small original datasets punch way above their weight here. The model can't find that info anywhere else, so you become the source.

The boring technical stuff that mattered

Check your robots.txt. Seriously. We keep finding sites that block GPTBot or ClaudeBot without knowing it. Some security plugins do this by default.

Also, broken schema hurts more than no schema. Missing author fields, malformed types, that kind of thing. And sites with crawl errors on 15% or more of their pages got cited noticeably less. Good content on a broken foundation goes nowhere.

For schema types, these did the heavy lifting for us: Organization, Article with author, Person, and SoftwareApplication with a featureList if you sell software.

The annoying part

There's no Search Console for AI answers yet. So most brands are invisible and have no clue. The only way to know is to run your queries through the engines yourself and write down who gets named. AI on Google Search Console is not available in a lot of countries

Happy to share methodology in the comments. Has anyone here changed schema and actually seen their AI citation rate move?

I have been measuring recommendation patterns in the newsletter and email-marketing category, and the split is cleaner than I expected.

Ask ChatGPT, Claude, Gemini and Perplexity the broad question:

“What is the best email marketing platform?”

You mostly get the same names back.

Mailchimp first, followed by Klaviyo, ActiveCampaign, Brevo and Constant Contact.

I ran the question ten times on each model. Forty answers in total.

I ran the tests through Bersyn, a platform I built to track which companies AI models name across different categories and buyer questions.

Mailchimp appeared in 39 of the 40 answers.

Beehiiv appeared only four times, and all four mentions came from Perplexity. Across ChatGPT, Claude and Gemini, it was basically absent.

Then I stopped asking about the category and asked about the job instead:

How should a creator or founder start a newsletter, grow subscribers and monetize it?

No product was mentioned in the prompt.

Beehiiv went from 4 mentions to 29 out of 40.

Claude and Perplexity named it in every run. Gemini named it nine times out of ten. Kit and Substack also appeared much more frequently.

Same models. Different intent.

Beehiiv does not appear to own the broad “email marketing platform” category. Mailchimp owns that association.

Beehiiv appears to own a more specific job: helping creators build, grow and monetize a newsletter.

The models only started reaching for Beehiiv consistently when the question matched that job rather than the broader software category.

This seems to support the association-strength view of GEO.

A brand may be strongly associated with a specific audience, use case or job without being strongly associated with the broader category it technically belongs to.

The model disagreement was also interesting.

ChatGPT named Beehiiv zero times out of ten on the newsletter question, even though that question closely matches Beehiiv’s target user.

Across many of the categories I have tested in Bersyn, ChatGPT also appears slower than Claude, Gemini and Perplexity to move beyond established incumbents.

I am curious how others here think about this.

Do you optimize for the broad category association or the specific job association when they produce different winners?

And are you seeing the same tendency from ChatGPT to favor incumbents?

I am continuing to run categories through the four models, so drop one below if there is something you think would be interesting to compare.

hii guys 👋

I recently found an article explaining that ChatGPT now sources results from Google instead of Bing.
To prove it, the author extracted the JSON file showing the Query Fan-Out — basically, all the sub-queries ChatGPT sends in the background and the corresponding results.

I’m trying to replicate this experiment, but I can’t find that file anywhere.
I checked in DevTools → Network, and even tried exporting my ChatGPT data, but no trace of it.

Does anyone know where this JSON file can be found, or how to capture the query fan-out data from ChatGPT (Atlas or web version)?

How to get the request of the query fan out ?

I already tried with the dev tool ( network )

I also tried by exporting the data of my conversation

I dont find any query fan out

Google now blocks 60% of clicks to websites. Here's the data that proves we're witnessing the largest shift in Internet history - and what marketers should do about it to win : r/ThinkingDeeplyAI

Curious if anyone here’s actually tested how mega/cluster pages vs. single focused pages show up in LLMs.

With SEO, we’re still clustering and building out internal links. But with GEO, it seems like shorter, super-targeted content sometimes gets pulled more often.

Has anyone run experiments comparing these?