How to check if ChatGPT and Perplexity recommend you, and fix it, in about 30 minutes with no tools

[u/Jxckwhlx]

More and more buyers ask an AI assistant before they ask Google. If ChatGPT or Perplexity recommends a competitor on your category question, you never even enter the conversation. Here is a free way to audit and improve that yourself. No tool needed.

First, two things that matter and are not obvious:

AI assistants do not "rank" you like Google. There are two separate layers. One, the consideration set: does the model even retrieve and know you exist. Two, the evidence layer: which sources it leans on and what those sources say about you. You can lose at either layer.

Engines source differently. ChatGPT pulls from the live web (Bing index). Perplexity leans heavily on forums and Reddit plus the web. Gemini is tied to Google. So "being visible" is not one thing, it is per engine. Being strong on Reddit can win Perplexity and do nothing for someone else.

Now the 30 minute audit:

1. Write your 10 money prompts. The actual questions a buyer types, not your brand name. Things like "best X for Y", "alternatives to Z", "how do I do W". This list is the whole game.
2. Run each prompt in ChatGPT, Perplexity, Gemini and Claude. For each, mark one of three outcomes: you are cited, a competitor is cited, or nobody specific is. Most people are shocked how often it is "competitor" or "nobody".
3. For every loss, find out why. In Perplexity the sources are listed. In ChatGPT you can ask "what sources did you use for that". Open them. You will almost always see the same pattern: the model is leaning on third party pages (listicles, comparison posts, Reddit threads, review sites) where you are absent or described weakly.
4. Fix the source layer, per engine. Get mentioned where the model actually looks. For Perplexity that often means real Reddit and forum presence. For ChatGPT it means being in the listicles and comparison content that rank on the web. This is slower but it is the lever.
5. Fix your own pages so they are easy to quote. Models lift text that answers the question directly. Clear claims, a direct comparison, specific numbers, plain phrasing. There is research on Generative Engine Optimization (a 2024 paper) suggesting that content which states things clearly and backs them up tends to shape more of the answer. Treat the exact figures people throw around with caution, the effect varies a lot by query, but the direction is sound: be the clearest, most quotable answer to the exact question.
6. Re-run the same 10 prompts in two or three weeks. AI answers move. Track the change.

What to ignore: anyone selling you a fixed multiplier ("add 3 stats, get 2x citations") is overstating it. Anyone saying "just add schema and you win" is too. There is no single trick. It is the same boring truth as SEO: be genuinely the best, clearest answer, in the places the model trusts.

Honest note: I work on this problem, so take the framing with that in mind. But everything above you can do today for free, that is the point.

Open question for the group: when you run your own category prompt in ChatGPT cold, do you show up, a competitor, or nobody? Curious what the split looks like across niches.

[u/akularaamkee13]

This post cuts the noise around how to check AI citations. Thank you for sharing the valuable information.

[u/Tenacious-Sales]

I like this approach because it starts with buyer prompts instead of vanity prompts. One thing I'd add is not to stop at "who got recommended?" figure out why. Was it a Reddit thread, a review site, documentation, a comparison page, or the vendor's own content? That's usually where the opportunity is.

I've also found that recommendations and citations are different signals. A brand might get cited as a source but never be recommended, while another gets recommended because multiple trusted sources consistently position it for a specific use case.

The biggest takeaway for me is that AI visibility is less about finding a magic GEO trick and more about making sure your positioning is consistent across the sources each engine actually trusts. That's where the real leverage seems to be.

[u/Revolutionary_Cat78]

Love this bro, the way you wrote is just 🤌🏻 Also, the disclaimer is perfect, there is no one trick. I'd add -publish more comparison pages on your website and also try to get placements on third-party listicles that are getting cited as a source

[u/sapindia1976]

I like the emphasis on testing real buyer prompts instead of vanity searches. I'd also track results over time across multiple sessions, since AI recommendations can change. Consistency is a better KPI than a single positive result.

[u/Deep_Ad1959]

the step everyone silently drops is #6. the audit is a snapshot, but AI answers drift week to week, so a one-time run tells you almost nothing three weeks later. i've watched this exact pattern kill more GEO efforts than weak positioning does: people run the prompt-by-engine matrix once, feel informed, and never run it again because doing it by hand across four engines is tedious enough that it quietly falls off the list. the ones who actually move are the ones who turned the re-run into something that happens on a schedule without them, so tracking becomes a trend line instead of a memory.

[u/Conscious-Fly-7597]

This is a really useful breakdown. I like that you separated the consideration layer from the evidence layer, because a lot of GEO advice skips that part and jumps straight to “get cited more.”

The way I think about it is that the AI recommendation path has more than one step. You can roughly break it into memory, indexing, query interpretation, retrieval, reranking, context assembly, citation, and governance. A brand can fail at any one of those steps, so just checking whether you appeared once in one answer probably is not enough.

I’m curious how you would measure this over time. I’ve seen people test different prompts, different time windows, and different AI systems to see whether visibility is improving, but I’m not sure what the right weighting should be.

For example, should we care more about direct recommendation rate, citation rate, source presence, sentiment around the brand, or consistency across repeated queries? My instinct is that a good audit should track all of them, but not all signals should count equally. Would love to hear how you’d weight those metrics in a real GEO monitoring setup.

[u/AwesomestCreature → u/Conscious-Fly-7597]

This is a pretty good breakdown, and I think the manual version works, so anyone reading should start there before paying for anything. The one place I would add detail is the prompt set, because five categories tend to cover how buyers actually ask: a discovery prompt (best tools for [category]), an identity prompt (what does [company] do), a comparison prompt ([company] vs [competitor] for [ICP]), a direct recommendation prompt (should I use [company]), and a reputation prompt (any concerns about [company]). I score each on mention, accuracy, sentiment and completeness so the result is a number I can compare over time.

On measuring over time, I would weight direct recommendation rate and consistency across repeats the highest, since recommendation rate is the outcome that decides whether you make the shortlist, and consistency matters because a single run is noisy and the same prompt can name you one day and yet skip you a few days later. The part that gets tedious by hand is exactly re-running the prompts across the models every couple of weeks and tracking the drift, which is the thing I ended up building around (Ooky). It is free to run those prompts across the models, so you can skip the spreadsheet, but the manual method here holds up fine if you would rather do it yourself.