How to check what AI search says about your brand
A framework for measuring what AI search engines actually say about your product.
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Most teams tracking AI search focus on one question: are we mentioned?
They open ChatGPT or Perplexity, type âbest project management tool,â and count how often they appear.
Roughly speaking, thatâs visibility tracking.
Sure, it matters. But it answers only one question. (The easy one!)
Hereâs a harder one:
How does AI describe you? When a buyer asks about specific features or integration depth, what does the model say about your product?
That is perception. And most teams are not measuring it.
Read on if you want to get an edge.
Two prompt types
There are two types of prompts worth tracking in AI search.
1. Topical Prompts
They help you test whether you show up. They cover your category and use case.
âWhat is the best email marketing platform for e-commerce?â is a topical prompt. You track these to check if your brand appears, how often, etc.
2. Evaluation Prompts
They help you understand how you are being perceived.
They dig into specific capabilities and limitations buyers consider. Is AIâs perception of your product accurate?
Most teams only run topical prompts.
That tells you if you are in the room.
Evaluation prompts tell you what the room says about you.
Hereâs how evaluation prompts work
Every evaluation prompt sits inside a three-level hierarchy:
A Topic is a broad area.
For email marketing, that could be deliverability or automation.
For a CRM, it might be pipeline management or reporting.
An Evaluation Attribute is a specific, measurable criterion inside that topic.
Under deliverability, the attributes could be inbox placement, bounce management, authentication support or sender reputation tools.
For each attribute, you write one Evaluation Prompt using a consistent template:
Evaluate {Brand} on the {platform/product/solution}âs ability to {specific evaluation criterion}, {desired outcome or business impact}.
The template stays the same every time. What changes is the attribute and the outcome.
(Yes, it reads like a rubric. That is the point.)
An example
Say you sell email marketing software and deliverability is the top buyer concern. Here is how you break that single topic into seven evaluation attributes, each with its own prompt.
Run each prompt across ChatGPT, Perplexity, Googleâs AI Overviews, etc. Do this for every attribute that shapes a buying decision.
The result: a clear read on whether AI describes your product the way you need it described.
Authorâs Note: As Iâve explained in a previous note, any gaps between how you want to be perceived and how youâre being perceived is called Perception Deviation.
Things to keep in mind
Before I leave you, here are a few things to keep in mind when it comes to Evaluation Prompts:
Evaluation prompts measure perception, not visibility. This is about how AI describes you, not whether it mentions you.
Audit the cited sources alongside every answer. Every URL AI attaches to a response is either something you can fix or a content gap you should fill.
Track sentiment and sources together. Perception is the symptom. The sources AI pulls from are the cause. (Fix the source, fix the perception.)
Expect platform variance. Different engines pull from different retrieval sources. The same prompt will produce different answers depending on the platform.
Negative answers are an opportunity. Every âdoesnât support Yâ or âlimited capability in Zâ points to a specific source you can correct or content you should create.
Letâs wrap this one.
Final Thoughts
Most AI search measurement stops at counting. Mentions, Share of Voice, rankings, etc.
Useful numbers. But they are the floor.
Evaluation prompts go deeper.
They reveal how AI search engines perceive your product on the specific criteria your buyers weigh.
When that perception is wrong, they show you which sources to fix.
Keep that in mind next time you are building your AI search tracking system.
Thank you for reading todayâs note, and see you again next week.
Research Disclaimers and Limitations
GrowthWaves and its author are not sponsored by or compensated by any company mentioned in this note. This is independent editorial analysis and does not constitute investment, financial, or legal advice. The author may have relationships with, work with, or hold equity in companies referenced; however, no content in this piece was influenced, commissioned, or incentivized by any such relationship. AI tools were used as a research assistant in the preparation of this piece. All claims are sourced and linked throughout.