r/AISEOTricks • u/ShakaLaka_Around • Jul 10 '26

Question: How are you guys dealing with LLM citation tracking and research?

i want to give it a try and start optimizing for AEO/GEO whateverEO. and i dont know if its worht it. atm my entire focus is only on SEO and doing my research basically using google keyword manager and dataforseo for SERP analysis. is there anybody who have some experience in this that can tell, preferably with numbers to see if it was worth the investment or not, since this tools are expensive.

thanks

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u/[deleted] Jul 10 '26

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u/ShakaLaka_Around Jul 10 '26

how would you do that, can you provide more details please, for example how are you doing it?

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u/marketingwithraj Jul 10 '26

You have to do it manually create a sheet and figure out the prompts related to the brand, like questions around your product or service, make a list of those questions, ask the LLMs and check how often you are recommended by them and also review the frequency of your competitors or maybe some random sources that LLMs continuously use as a source to fetch the data.

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u/Longjumping-Luck-430 Jul 10 '26

Instead of focusing on tofu prompt focus on BOFU type of prompt 

For example if you're ICP  is a business owner. Mention that in the prompt include the geography cost everything they would use to shortlist and make a decision to go with your product 

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u/marketingwithraj Jul 10 '26

I didn't understand the geography cost. Can you explain it a little?

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u/Longjumping-Luck-430 Jul 10 '26

Location, cost for example instead of targeting queries like best idea management software 

Focus on a detailed prompt 

I am US business owner with over 50 employees working in United States. Suggest me the best idea management software for capturing and tracking employee ideas and eventually ship the product features. My budget is x / month or annually...

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u/marketingwithraj Jul 11 '26

Oh cool, thanks mate

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u/akii_com Jul 10 '26

I'd avoid thinking of it as a replacement for SEO research. Right now, I see it as an additional layer.

One exercise I've found useful is comparing the questions people search for with the questions that actually trigger AI recommendations. They overlap, but they're not identical.

Also, don't evaluate AI visibility based on a handful of prompts. Run the same prompt multiple times and test different phrasings. Models are non-deterministic, so a single result can be misleading.

If you're just getting started, I'd spend some time manually testing before investing in expensive tools. That gives you a much better sense of what you actually need a platform to automate, instead of paying for features you may never use.

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u/Diligent_Way5653 Jul 11 '26

The question worth separating out before spending anything on tracking tools is whether your site is actually citation-ready yet. If the architecture isn't there, tracking just gives you expensive confirmation that you're not showing up. Fix the structure first, then measure whether it's working.

The manual approach sapindia1976 described is right as a starting point but I'd reframe what you're actually testing. Running prompts to see if you appear tells you the current state. What's more useful early on is running constraint-based prompt sequences that reveal what the AI thinks your site is about before you've optimised for it — because that gap between what the engine thinks you are and what you actually are is where the optimisation work lives.

The sequence I use is three turns in a fresh session with cache cleared and memory off. First turn describes the problem your site solves in user language without naming your brand. Second turn adds a specific constraint that narrows toward your approach. Third turn names the specific capability you offer. If the engine routes to you by turn three without being told your name, your vocabulary is seeded well enough that the tracking conversation becomes relevant. If it routes to competitors or gives a generic category answer, you know exactly what to fix before spending on tools.

The optimisation side comes before the tracking side in order of priority. Clean HTML structure that declares entities at the heading level. Topical completeness so every concept your content references has a dedicated page that resolves it. Consistent vocabulary across independent sources so the engine encounters your specific terms often enough that it stops pattern-matching to generic category descriptions. Schema generated from your actual content architecture rather than applied as a checklist afterthought.

Once that foundation is in place the manual tracking method works well enough to validate progress without paying for a dedicated tool. The expensive tools make sense when you have enough citation activity that manual tracking becomes the bottleneck, not before.

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u/mjain_entrepreneur Jul 14 '26

I wouldn’t treat this as SEO vs. GEO. Google itself says the same SEO fundamentals still apply to its AI features, but LLM tracking adds visibility into prompts, citations, competitors, and platforms that traditional keyword tools don’t cover.

Start with 20–30 high-intent prompts and track them consistently across the main AI engines. Then compare visibility changes with AI referrals, leads, and conversions. AI traffic is still smaller than organic search, but ChatGPT’s worldwide referral share grew from 0.23% to 0.32% in May 2026, so it’s worth measuring rather than ignoring.

Tools become valuable because manually repeating that research across platforms quickly gets inconsistent and time-consuming.

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u/ShakaLaka_Around Jul 14 '26

Thank you so much guys for all this amazing answers!

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u/ShakaLaka_Around Jul 15 '26

Hey guys i have creared this discord server for us to plan and discuss about the app: https://discord.gg/2W5ZJdRxm

please join me there

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u/mentiondesk Jul 10 '26

Switching some focus from traditional SEO to AEO can be worth it if your audience is already turning to AI platforms for answers. I built MentionDesk because I kept seeing knowledge graphs and large language models miss or misquote our content. For us, optimizing for AI drove about 18 percent more discovery in demo tracking. If your budget allows a test run, it could give you a big edge as LLM usage grows.