I have seen a *lot* of AI prompt tracking tools popping up lately - tools that claim to be able to show you when your brand is mentioned in AI for target prompts. Some are VC-backed, a lot are side hustles. Unless you have tens of thousands of dollars to spend, they're all a waste of money. Here's why.

**What's a prompt tracker?**
A prompt tracker works very similarly to traditional SEO keyword trackers. A user will enter a set of prompts that they think are likely to feature their brand. The prompt tracker runs these against the AI model and summarizes the output to show when brands are 'cited', meaning included in the list of sources used to generate an answer, and also whether the brand is included in the answer itself. For most prompts the brands included in the summary are a small fraction of the ones included in the list of sources.

**How AI Chat Tools Make Recommendations For Brands (And How Each Step Makes Prompt Trackers worse)**
To understand why a prompt tracker is less like traditional SEO and more like just like dumping a bunch of money in a pit labeled 'Open AI', you need to understand how AI chat tools actually recommend brands, and how a prompt tracker works or doesn't work at each step:

Step 1: The user opens a chat window and enters a prompt.

Already there's a huge difference between how users engage with AI chat vs a search engine. The average length of an AI prompt is 22 words. There are a lot of ways to ask exactly the same question! If you are trying to track prompts the same way you track keyword SERPs you will be tracking exponentially more combinations.

Step 2: AI considers what it "knows" (from the model) and builds a series of web searches to run in parallel to gather data. This is known as 'query fan out'.

What's query fan out? When you enter a prompt into an AI chat that has access to web search, it will break the prompt down into several shorter, overlapping searches and run those. This process is known as 'query fan out' and it's based on a Google patent. It's a way for AI to control for variability in user prompts.

So the prompt "What are the best wireless headphones under $200?" Might result in three web searches:

• "Wireless headphones under $200"
• "Top rated wireless headphones"
• "Best budget wireless headphones"

Step 3: The search engine 'behind' the AI returns results. In this step the AI sends those fan-out searches to the search engine they built or partnered with. All of the major AI chat platforms do this:

ChatGPT -> Bing Claude -> Brave Search Perplexity -> Their own custom search engine Microsoft Copilot -> Bing, obviously Google Gemini -> Google, obviously

It's also worth mentioning that the citation that includes your brand might not be from your website at all! There's a very good chance if your brand is cited, the citation is coming from a third party; a reddit thread, a Quora answer, G2 or Yelp reviews, etc. And any sentiment from the source is likely to be applied to your brand in the summary. So if you got a bad review, AI might very well parrot that back to the user.

Step 4: AI enriches those results with other data sets This is the step about which the least is known. If your brand is part of the AI model's training set, it will already 'know' things about you, and might use that. Most prompt trackers don't tell you 'how' you're cited, just 'whether' you're cited. But if AI mentions your brand constantly and it's all bad, you probably want to know that too!

Step 5: AI synthesizes all of this into a comprehensive response. If you weren't already convinced that prompt tracking is a waste of time and money, this step will seal the deal. There are two huge factors in how AI generates responses that make the very concept of prompt tracking basically silly:

AI output is nondeterministic. Most things in the field of computer science are based on the concept of "same input gives you the same output". Imagine if Excel just used a different formula every time you asked it for a calculation!

AI however is different. If you ask AI a question twice you are not guaranteed to get the same answer. In fact you are almost guaranteed to not get the same answer.

If you are tracking prompts, this variability means you a) Can never be 100% sure that your website will be cited in the summary for any given use of the prompt even if it's a cited source, and 2) In order to feel be 90% confident (with a 5% margin of error) that the result you get is accurate, you have to test each prompt 271 times. At an expected cost of $0.37 per run that's about $100. PER LLM. PER MODEL.

The second issue with trying to prompt trackers is that ChatGPT the web platform and the API are very different! Most, maybe all prompt tracking tools on the market use the API. This makes a ton of sense because the API is designed for code integrations, and it is a violation of OpenAI (and every other LLM)'s terms of service to run automations against the web interface (not to mention ChatGPT usage is capped, even for paid plans).

But the API and ChatGPT.com are different, for one key reason: the system prompt. The system prompt is the set of instructions that the LLM considers alongside your message. It's the instructions that guide the LLM's behavior; always be helpful, don't tell the user how to make a bomb, that sort of thing.

When you post a message to the API there's no explicit system prompt. You can provide one, but the system prompt for ChatGPT is proprietary, unknown, changes frequently, and is by all accounts very complex. It also makes a huge difference in the output.

Although several of the cited brands and sources are the same, the output and experience is very different! If you're using the API for prompt tracking (which, legally, is the only way you can use an LLM for this), you're not getting anything close to the same output as a real user would.

Do This Instead Hopefully now you are convinced that tracking prompts isn't an option. But you can get good data on your likelihood of ranking for a lot cheaper.

Start with the prompts you want to track, and instead of tracking their output, query the LLM for this prompt:

Generate 8 distinct search sub-queries that together cover the user intent:
"[INSERT USER PROMPT HERE]"

Rules:
- Each sub-query should target a different slice of the overall intent (for example: if the user intent is [best wireless headphones under $200], then slices might include reviews, noise cancellation, comfort, battery life, durability, brand comparisons, or call quality).
- Output in a simple CSV table with columns:
  ID, Query, Slice, Priority
- ID = short unique label (Q1, Q2, …).
- Query = the exact search query text.
- Slice = short description of what part of the intent it covers.
- Priority = integer 1–10 (1 = highest).
- Do not add any commentary or prose outside of the table.

This should give you something like this:

ID,Query,Slice,Priority
Q1,"best wireless headphones under $200 2025 reviews","fresh ranked reviews",1
Q2,"noise cancelling headphones under $200 comparison","ANC focus",2
Q3,"battery life wireless headphones midrange $150-200","battery/durability",3
Q4,"comfortable lightweight over ear headphones under $200","comfort/ergonomics",4
Q5,"Sony JBL Anker Soundcore under $200 models","brand scan",5
Q6,"wireless headphones under $200 mic quality calls","voice/call performance",6
Q7,"gaming wireless headsets under $200","gaming use case",7
Q8,"refurbished vs new wireless headphones under $200","purchase context",8

Drop these into your favorite traditional SERP tracker, keeping in mind that ChatGPT uses Bing Search. Then optimize for them the way you always have.

Now set up an AI search analytics tool. There are a few out there at different price points. Your goal here is to trck two things:

• Your total AI citation volume, per website page and per model
• The percentage of those citations that result in a clickthrough to your site

Once you have your SERPs and this data, you are back to doing mostly pure SEO, and you have a number to show how your work is increasing your AI 'traffic'. You also saved a few hundred or potentially thousands of dollars a month.

This isn't to say there aren't other AI-specific SEO techniques to follow - building and monitoring third-party mentions, creating AI-optimized content, ensuring your JSON-LD is up-to-date, etc. But you can redirect the money you aren't spending on a prompt tracker to this work and see more results from it.

If you've used a prompt tracker I'd be curious to hear your perspective and whether this seems like a viable alternative.