Prompt tracking gets expensive fast. You want to monitor AI visibility everywhere, but new prompts inflate costs and muddies the data. 

You don’t need more budget. What you need is a strategy to trim bloated lists and identify prompts that support your brand goals. 

In this post, I'll show you how to build a prompt tracking strategy that measures visibility without burning cash.

Prompt tracking is the process of monitoring how your brand appears in AI answers for a set of saved prompts. 

Instead of tracking Google rankings, you track mentions, sentiment, and how often a model surfaces your brand for relevant topics.

Monitoring your visibility in AI searches is critical to establishing KPIs for generative engine optimization success.

Done right, prompt tracking shows how visible you are during discovery, especially for non-branded queries where users ask for comparisons or recommendations without naming a specific brand.

There is a temptation to track every prompt for every keyword. Resist this urge. Cluttering up your prompt profile can quickly inflate costs and exhaust resources because:

• Using prompts that don’t match the specifications of the tool may give you false positives and misleading results 
• Tracking too many prompts can make analysis unwieldy and repetitive 
• Choosing prompts that don't align with your business goals reduces ROI
   

1. Qualify your audience

Use the data you already have to see which LLMs your audience prefers before spending money on prompt tracking. 

Research by Wix shows that LLMs' preferences can vary by gender, age, and country. For instance, Millennials are more likely to shop using AI, and women are much more likely to use ChatGPT than to use Grok.

This means that if you’re focusing your efforts on a channel your audience doesn’t use, you may not get as much value from your prompt tracking as you would otherwise.

So, how do you identify the AI tools that your audience is more likely to use?

 Start in GA4 to identify AI traffic, then use the data to prioritize which LLMs to track:

• Check GA4 to confirm which platforms your users prefer
• Filter by region
• Review which content gets AI traffic
• Identify topic trends

To do this in GA4, go to Reports → Acquisition → Traffic acquisition.

According to Brie Anderson, Google uses predefined channel groupings, so AI search traffic would likely fall under AI Tools, Unassigned, and Referral.

You can get more granular by switching the primary dimension to Session source/medium. 

From there, look for LLM referrers and filter the report to isolate AI traffic. Avoid the temptation to only view the popular LLMs like ChatGPT and Gemini, as you might miss out on new referral sources you’re not aware of.   

Have a look to see if there is a particular platform bias. If so, it could be worth allocating more of your prompt-tracking efforts here.

Next, filter by region in GA4 to spot differences in platform usage and query language across markets. This is important because LLM usage varies by country. For instance, UK users are more likely to use AI daily than US users. Even within the same language, regional languages can impact the way people search. 

Now let’s drill down into the content. Review which landing pages already receive AI traffic and look for topic patterns across those URLs. If you notice that there is a particular topic that drives AI traffic, consider leaning into creating more pages like this and optimizing your prompts to reflect this interest. 

Once you know what content AI visitors are landing on, you can build a lean prompt list that reflects market demand. Use the data from GA4 to choose a model when setting up prompt tracking in Moz AI Visibility tool.

Here’s how:

Go to Moz Pro, select AI Visibility on the left-hand menu, enter your brand name (e.g., Toyota), and click Check Visibility.

You’ll be able to choose either ChatGPT or Gemini models to track in Moz AI Visibility and compare performance across both models.

From here, you can add up to 5 related terms for your brand and up to 3 competitors, along with their related terms (optional). Next, add a list of topics for prompt generation and click Generate Prompts. But before you do that, make sure you prepare your brand topics.

2. Prepare your brand topics

Before building a prompt list, it’s important to understand what the tool thinks your brand is about and what it counts as a branded term. 

Go to Moz Domain Overview, enter your URL, and look at the domain search theme. 

The theme is the tool’s assumption about your site; treat it as a baseline.

Next, scroll down to Domain Keyword Topics and use the list to expand branded queries. 

For example, Toyota’s branded terms include model names like Highlander and Tacoma, while Kia’s branded terms skew toward broader brand queries like Kia lease deal and Kia electric car.
 

Check for regional differences before finalizing your branded list. For example, in the US, Toyota's brand interest is more model-focused (Toyota Corolla), while in the UK, it's more brand-focused (Toyota vehicle manuals). 

These differences might seem minor, but they make all the difference if you’re a local business. That’s why you should use Domain Keyword Topics to confirm branded terms, validate topic themes, and identify any regional keywords you’re missing.

Once you have the branded term list, input it as alternative branded keywords when setting up your AI Visibility prompt tracking in Moz.

3. Identify competitors based on entity signals

The competitors you assume may not match what Google associates with your brand, so validate them before setting up prompt-tracking benchmarks. 

Search your brand on Google, and review the People Also Search For panel. The brands listed there reflect your closest entity competitors in the Knowledge Graph.

If you work with a local business, use the same method through Google Business Profiles. Scroll to the bottom of the profile and review the People Also Search For section to see the competitors Google associates with your brand.

Once you’ve found these competitors, update your project settings in Moz AI Visibility so your tracking reflects entity relationships rather than internal assumptions. 

For example, I originally assumed that Nissan, Kia, and Hyundai were Toyota's closest competitors, but based on People Also Search For analysis, I’ve changed the competitors to Nissan, Honda, and Mazda.

The list won’t perfectly match ChatGPT or Gemini, but it gives you a better view of the competitors that Google and your audience associate with your brand.

4. Confirm what your prompt tracking tool is measuring 

You need to understand the tool's parameters, how it works, which models it tracks, and how frequently it updates. Once you understand those constraints, you can prune your prompts for more valuable outputs.

Let’s use the Moz AI Visibility tool as an example. First, look at the model. In this view, the models are ChatGPT and Gemini, so that’s the first decision you need to make.

Next, look at duration. Here, we can see 30 days due to the recent setup. Moz tracks up to 180 days, so data visibility depends on tracking length. 

Don’t expect data immediately. If you add prompts on Tuesday and the tool refreshes its data on Mondays, you won’t have new information until the next update. Working with a schedule like this should be fine for most businesses, but for brand-new, time-sensitive queries, it may be worth testing manually, while you wait for the data to populate.

Finally, confirm what the tool is tracking. In this example, the main metric in this tool is brand mentions. 

It shows that Toyota appears in 38% of LLM-generated responses, alongside factors such as average position in response length and its ranking among the brands analyzed. 

Once you understand the model, refresh cadence, and visibility metrics, you can choose prompts that fit those constraints and avoid interpreting normal update behavior as performance data.

5. Do topic research 

Your topic research should come from frequently asked questions. Tap into the primary sources for topic research and user queries within your business. Start with the language your customers already use, because it produces better prompts than anything you invent in a spreadsheet.

Pull topic ideas from your internal teams:

• Sales calls
• CRM
• GBP questions
• Product reviews
• Audience feedback
• Forums like Reddit

Lashay Lewis has a useful guide for finding high-intent BOFU keywords to sharpen your list. 

Next, add questions from your Google Business Profile and pull wording from product reviews so your prompts match how people describe problems in real life.

Once you’ve captured the language, scale it with keyword data. Log in to your Moz Pro account and navigate to Keyword Research > Ranking Keywords. Enter your brand URL to generate a list of keywords you rank for and export the list as a CSV.

Use AI tools like ChatGPT to cluster queries and identify non-branded prompt variations. Here’s an example of how I did it.

Moz AI Visibility tool allows you to enter four topics to generate prompts for tracking brand mentions. I’ve used some of the non-branded topics ChatGPT highlighted, including car reliability and fuel efficiency car.

6. Avoid prompts that create false positives

Separate prompts into three types: branded, comparison, and non-branded.

Branded prompts trigger your brand name and inflate visibility, which doesn’t tell you whether you’re being recommended naturally. 

For example, if I ask a question like “Do Toyotas have good resale value for families?”, the natural response will be to mention Toyota, because the question is about Toyota. If I'm measuring mentions, then every single question that starts and centres on my brand name is a wasted prompt.  

I could simply increase the number of questions that include my brand name and thereby improve my mentions.  Branded prompts definitely have value, particularly around sentiment, but it's worth reviewing your prompts to ensure that they're not telling you needless information. 

Consideration around branded prompts should also apply to competitors. For instance, if we used the prompt, “Do Mazdas have good resale value for families?”, we are extremely unlikely to see a mention of a rival brand.

Comparison prompts sometimes trigger your brand, but they still skew results depending on how the question is phrased. So, a question like “how does the comfort of Hyundai cars compare to for road trips?”,  might mention other car types, or go into detail about Hyundai. 

If you are tracking mentions, the real opportunity is in non-branded prompts. Non-branded prompts are valuable because this is where models recommend solutions to potential buyers. 

A question like  “What hybrid cars are better for long-distance driving?”, is open field because it will certainly trigger a list of brand names. And it’s a valuable measure because whether you’re included in the list or not is something that can be earned and managed. 

Focus on prompts that trigger brand lists and recommendations, such as:

• Best options
• Comparisons
• What should I choose

7. Prune prompts aggressively

Start by removing branded prompts, as they can cause false positives when you are tracking mentions.

Next, remove prompts that are off-topic, have no commercial intent, or are unlikely to trigger brand mentions. If a prompt never produces brand lists or recommendations, it won’t help you measure visibility, and it doesn’t deserve budget.

Cut anything that doesn’t match your market. If you’re tracking prompts in the wrong region, using the wrong spelling, or phrasing, your data becomes unreliable because you’re measuring behavior your audience doesn’t have.

Also, avoid prompts tied to fast-changing, real-time topics if your tool only updates weekly. The answers change too quickly, so your report won’t reflect what users saw that day, and the data becomes hard to trust.

Keep prompts tied to user needs and common comparison patterns, especially high-intent phrases. A smaller prompt set gives you clearer benchmarks and signals you can act on.

This space is dynamic, so expect change. You need to test, try things, and see what happens.

Start by testing topics in groups. Cluster prompts around high-intent topics, monitor results, address weak areas, rinse and repeat. Prompt tracking works better when you treat it like CRO, not keyword research. Go in with a goal and a hypothesis you want to test.

Also, test prompts by type. If “best” lists aren’t working, track those. If you think reviews are the gap, track review-style prompts. You’ll get a better handle on what the model is doing and where you should take action.

Finally, keep aligning prompts with customer queries. Use what you already have from emails, support, client feedback, and forums to write prompts humans would use. Then create and update content to improve visibility for low-performing topics and monitor growth over the next refresh cycles.