r/GenEngineOptimization

Statistics is a funny thing. When something grows from 0 clicks to 3 clicks, it makes it a 300% growth rate, to be sure. :)

AI traffic is 0.3% of all traffic for websites, on average, after three years of sending traffic, but we are still excited about reporting that :)

To be sure, traffic is not going to be a major AI visibility metric for a foreseeable future, since AI Answers are not designed for clicking. But it is nice that it grew to 0.3%

https://preview.redd.it/tdniyle68gah1.jpg?width=2048&format=pjpg&auto=webp&s=9f1048cdf7a9dd75b41bdc0dd8a3b74299386c5c

Source: Seranking

Read a study that showed by a large margin, earned citations and social & community citations outweighed all on-page content factors. This was tracking 6 major AI models across 31 industries.

(Question 1) How are people getting these off-site mentions?

Are people expanding their PR teams?
Encouraging more cross-collaborating between SEO and PR teams?
Experimenting with any social influencer/creator marketing (B2B or B2C)?

(Question 2) Between citation volume and compounding what seems to be driving the most results?

I've also read that AI systems reward consistent presence, aka entity building, in a select few related domains. On the other hand, getting as many citations as you can is also an effective strategy.

Am I missing anything or thinking about this wrong?

Been doing a deep dive into AI search visibility after noticing something weird: my paid ROAS was holding steady but organic felt like it was slowly going nowhere.

Ran the obvious checks. No manual penalties. Content quality fine. Backlink needs a bit of work. Still couldn't explain the plateau.

Then I tried something I hadn't done before - I opened ChatGPT, typed in five buyer-intent queries for my category, and tallied every brand that got mentioned across each one.

What I found:

One competitor was showing up in 4 out of 5 queries across ChatGPT and Perplexity. Not a huge brand. Actually smaller than us by most metrics. But they had something we didn't - a consistent editorial footprint. They'd been featured in 3-4 niche publications that kept getting cited as sources in AI answers.

We had zero of those citations. Not because the coverage wasn't available, we just hadn't prioritised it. The thing about AI search is that citations compound. That competitor has been building that footprint for probably 12-18 months. We were starting from scratch.

But it's also fixable. One well-placed editorial feature can shift your citation rate within 60-90 days. We got one piece picked up by a mid-tier industry publication last month, already seeing the brand mentioned more consistently in Perplexity results.

Anyone else been tracking this? Curious what categories people are finding the most competitive in AI search right now, and who's dominating them.

(For context: I built a tool called DaitaFix that monitors this automatically after running into this problem myself, happy to share more on the methodology if useful.)

I’ve seen from a lot of sources that adding more FAQ (count) and word count around 80-100 and clear detailed answers for a highly asked question is a good GEO signal.

So we’ve been doing it for our blog posts for my business. Now our content team has issues with how readable the FAQs really are. So I’d like to know how can I actually measure the impact of making FAQ changes on my pages.

I tried taking the exact question from an FAQ of my page and search it incognito but we are not the page that gets cited most time in AI Overview.

Does anybody have insights here? Would love to hear as to what argument I can give for continuing longer and more FAQs for my pages.

𝗪𝗵𝘆 𝗱𝗼𝗲𝘀 𝗖𝗵𝗮𝘁𝗚𝗣𝗧 𝗸𝗲𝗲𝗽 𝗿𝗲𝗰𝗼𝗺𝗺𝗲𝗻𝗱𝗶𝗻𝗴 𝗺𝘆 𝗰𝗼𝗺𝗽𝗲𝘁𝗶𝘁𝗼𝗿 𝗼𝘃𝗲𝗿 𝗺𝘆 𝘀𝘁𝗼𝗿𝗲?

I kept checking ChatGPT for my product category and my competitor kept showing up every time. Not me, even though I had better reviews and more products. Took me a while to figure out why.

A study this month confirmed that pages updated within the last 30 days get 3.2x more AI citations than older content.

Most of my product pages hadn't been touched in six months. That was pretty much the whole problem.

What actually worked:

I spent a Saturday refreshing my top 12 product pages. Didn't rewrite them, just updated the copy, made the pricing current, added today's date. Felt too simple to matter.

It mattered.

The catch:

The second thing I found: comparison content is basically catnip for AI. I added a simple section to each page showing how my product compared to alternatives. AI engines heavily weight structured comparison info when deciding what to recommend.

Bigger picture:

AI-referred orders on Shopify grew 13x year over year in Q1 2026.

ngl this is not a future thing, it's happening now. Stores are winning and losing based on whether an AI assistant recommends them.

After doing all this research and a lot of manual work, I did eventually find an app that basically does it all for you. It's got a free tier that does some basic optimization but the paid tier (like pretty much anything) is actually where it does the most optimization and even generates blog content for your brand with your own brand guidelines, voice and for whatever specific keywords you want based on Google SERP data.

The app is Gimmie AI. and yes I will shamelessly share my referral code here (c8mrfe-rf-245ef8) as well which gives us both a free month of the paid tier because most of us are boot-strapped and a free month helps. Though, 30 days may not be enough to see crazy results, you should definitely see a bump in your rankings within that time.

Has anyone else gone down this rabbit hole? Curious if it's a content problem, a data problem, or something else for you. lmk what's been working.

TLDR: Updated product pages monthly and added comparison sections, got 3x more AI citations. AI-referred orders on Shopify grew 13x YoY so this actually matters now. Found Gimmie AI automates the whole optimization, free tier available.

Hello, not sure if this is the correct forum, but will ask anyways :)

There's a lot of talk about optimzing your page, product, etc so ChatGPT and the other LLMs will recommend your products.

However, I couldn't find much about how to optimize your API, MCP, llm.txt, etc. to make an agent choose your tool over your competitor's.

For example,
Let's say a doctor asks an agent to create a personal website with a platform to book an appointment, so the agent goes and searches for a platform that does that and finds BookingPlatformX and BookingPlatformY, and goes for BookingPlatformY. The question I'm trying to answer is why and how could BookingPlatformX optimize to be chosen next time..

Is there any way to analyze this?

While some brands are interested in GEO, I feel like most SMBs—excluding the big players—will likely focus on channels that drive immediate performance. So, I’m curious about the actual level of interest in GEO in Ecommerce space right now.

There are general GEO services out there, but there doesn't seem to be a tool specifically tailored for e-commerce. If there's enough demand, I’m thinking about building one

How do you track if ai chats recommend your products? Seems like chatgpt's approach to suggesting products is still changing. Has anyone managed to properly track it?

AEO software pricing ranges from free trials to enterprise contracts of $500-2,000+/month (HubSpot) , and the pitch is almost always the same: plug in your brand, get a visibility score across ChatGPT, Perplexity, Gemini. Sounds clean.

What it quietly assumes is that someone already knows which prompts are worth testing. Most teams skip straight to buying the tracker without ever validating whether their prompt list reflects how people actually talk to AI assistants versus just keyword-shaped questions. You end up with a dashboard that looks authoritative but is measuring noise.

I started treating this as its own separate step - using existing ranking data to build out a prompt list, prioritized by which pages actually deserve a citation and which funnel stage each question sits in. Ended up automating it for myself because doing it manually for a real keyword set isn't realistic.

If anyone wants a quick gut-check on where they're already showing up in AI answers, happy to run that for free. I just don't offer ongoing monitoring for free since that's the part that actually needs upkeep.

Does anyone else think the industry jumped to the tracking layer too fast?

Also, DM me if anyone needs which type of prompts and what prompts you're getting cited for.

GEO (generative engine optimization) is a huge opportunity for brands right now. Referrals from ChatGPT, Perplexity, and Claude account for 30% of referral traffic and have a 4-25X higher conversion rate than search traffic. First movers will win here.

I've been auditing storefronts for this, and the gaps are wild. Brands with strong SEO and on-site conversion have almost no AI search presence because their product descriptions are thin, or their structured data is a mess, or their brand just isn't being talked about anywhere an LLM can find.

If you want to see where your store stands on both, I built a tool that crawls it, scores it, and gives actionable fixes. Happy to share it in the comments.

Here are my findings from the 10 most recent audits:

• Average GEO score was 37 out of 100. Lowest was 26, highest was 48. No store cleared 50.

• The schema gap is the big one. Only 1 of the 10 had a real product schema on its product pages. Not one had a review or rating schema, so none of their star ratings can surface when an AI is comparing products. Most were on Shopify, and the default theme schema just isn't rendering in a way the crawlers can use.

• The access side is what surprised me. All 10 had an llms.txt file, and none of them blocked the major AI crawlers. The engines can walk right in. There's almost nothing structured waiting for them once they do.

• Content was thin on the same axis. Not one store used question-phrased headings, the kind that match how people actually ask an AI about a product. None had a comparison table. Half had some FAQ content.

These stores convert fine and look good; however, they're sitting on a next-to-zero readiness for the fastest-growing channel.

Hi Everyone,

I would like to understand as we have Google Trends to undersand the queries searched by the users. Is there a way I can fetch the queries for ChatGPT, Gemini, Perplexity and Claude.

As Google Trends, has the way to look after the keywords, duration, location etc.

So, if anyone has idea to understand the realtime queries it really helps me to understand the top trending queries.

I've been seeing a lot of confident takes about AI Overviews and GEO lately. Things like, answer questions in the first paragraph, target conversational queries, build topical authority, use schema aggressively, get cited by high-authority sources, E-E-A-T signals, FAQ sections with exact-match formatting. The list goes on.

But I've also read in a few places that a lot of this is just correlation, that the sites getting cited in AIO were already strong before GEO was even a conversation, and most of the "optimization" people are doing isn't really the reason they're getting picked up.

To all the experienced experts, what's genuinely moving the needle for you? Are you writing for the AI or just writing really good content, and it happens to get pulled in AI overviews?

So I was auditing my own site's robots.txt today and noticed something kind of wild.

A lot of sites — especially ones that haven't touched their robots.txt in a few years — are blocking GPTBot, ClaudeBot, PerplexityBot by default. Not on purpose. Just because those bots didn't exist when the rules were written and the catch-all `Disallow` is doing its thing.

Like I scanned a Shopify memorial gifts store the other day. Score was 35/100. Turned out GPTBot was blocked and they had zero llms.txt. Fixed both, score went to 86. That was maybe 30 mins of work total.

For anyone who doesn't know — llms.txt is basically a plain text file at yoursite.com/llms.txt that tells AI models who you are, what you do, how to cite you. Think robots.txt but for LLMs. Without it the model is just guessing from your HTML and honestly the guesses are not great.

Quick things to check:

- yoursite.com/robots.txt — ctrl+f for GPTBot. not there? probably blocked.

- yoursite.com/llms.txt — if it 404s you don't have one

Not saying this is going to 10x your traffic tomorrow but AI referral traffic is growing and it costs basically nothing to fix. Figured I'd share since I haven't seen many people talking about the robots.txt issue specifically.

Anyone else been looking into this stuff?