The first thing every brand does is ask ChatGPT about itself. That's the least useful question you can track. The ones that decide deals never mention your name at all.
When a brand first gets curious about AI search, the ritual is always the same. Someone types their own company name into ChatGPT, reads the answer, and feels either relieved or alarmed. "What is [our brand]?" Done. Box checked. We know what AI says about us.
Except that's the one question your buyers almost never ask. By the time someone types your exact name into an assistant, they already know you exist; they're validating, not discovering. The questions that actually decide whether you win or lose in AI search are the ones where your name doesn't appear at all, the category questions, the comparison questions, the problem questions. Those are where a buyer with no prior knowledge of you gets handed a shortlist, and where you're either on it or invisible.
Most brands are monitoring the one question that flatters them and ignoring the dozens that matter. Let's fix which questions you track.
Short answer: what questions should I track in AI search?
Track the questions your buyers actually ask on their way to a decision, not just your brand name. That means five types: branded ("is [you] good"), category ("best tools for X"), comparison ("X vs Y," "alternatives to X"), use-case ("best X for [situation]"), and problem-based ("how do I solve X"). The category, comparison, and problem questions matter most, because that's where buyers who don't yet know you decide who to consider.
Key takeaways
- Branded queries are the least informative. People asking about you by name already know you. Useful for accuracy checks, weak for growth.
- Category and comparison queries decide deals. They're where undecided buyers get a shortlist, and where being absent costs you the most.
- Problem-based queries catch buyers early. People often describe a problem before they know the category or any brand names.
- The right question set is specific to you. It's built from how your actual buyers talk, not a generic template.
The five types of questions that matter
Think of the questions worth tracking as a funnel, from people who've never heard of you to people about to choose you.
Problem questions. The earliest stage. A buyer describes a pain without knowing the solution category or any brands: "how do I stop losing leads to competitors," "why is my site slow." If an assistant's answer to these surfaces your category and, ideally, you, you've been discovered at the very top of the funnel by someone who didn't know what to search for. Almost nobody tracks these, which is exactly why they're an opportunity.
Category questions. "Best tools for X," "top X software," "what should I use for Y." This is the core discovery moment: a buyer knows what kind of solution they need and asks the assistant to name options. The answer is a shortlist, and whether you're on it is close to the whole game. If you track nothing else beyond your own name, track these.
Comparison questions. "X vs Y," "alternatives to X," "is X better than Y for Z." Late-stage, high-intent, decision-adjacent. These reveal how you're positioned against specific competitors and whether the assistant frames you as the better or worse choice. Track the comparisons involving you and your main rivals.
Use-case questions. "Best X for small teams," "X for enterprise," "cheapest X for beginners." These are category questions sharpened by a specific situation, and they're where a well-positioned niche brand can beat a bigger generic one. Track the use cases you're genuinely best at, because that's where you can win even against larger competitors.
Branded questions. "Is [you] good," "what do people think of [you]," "[you] reviews." Last, and least, for growth, but still worth tracking for a different reason: accuracy and sentiment. This is where you catch AI saying something false or unflattering about you. Useful as a reputation check, not as a discovery metric.
Why the branded-only habit is so misleading
The reason "just check your own name" feels sufficient is that it's easy and it usually returns a reasonable answer. Of course the assistant can describe you when you hand it your exact name; you've done all the work of identifying yourself. It's like judging your sales team by how well they handle inbound calls from people who already asked for you by name. Reassuring, and almost irrelevant to whether you're growing.
The queries that actually grow a business are the ones where the buyer brings a need, not a name. Those are harder to face, because that's where you find out you're not on the shortlist, not compared favorably, not surfaced for the problem you solve. Which is precisely why they're the ones worth tracking. The uncomfortable questions are the informative ones.
How to build your actual question list
You don't need hundreds of queries. You need the right few dozen, built from how your buyers really talk. Here's the practical way to assemble them.
Start with your category, phrased how buyers phrase it. Not your internal jargon, the words a customer would use. If you sell "revenue intelligence software" but buyers ask about "sales forecasting tools," track the buyer's phrasing.
Add your real competitors by name. Build the comparison queries: you versus each main rival, and "alternatives to you." These are uncomfortable and essential.
List the problems you solve, in customer language. Mine sales calls, support tickets, and the questions prospects actually ask. Each real problem is a problem-query worth tracking.
Add your genuine use-case strengths. The specific situations where you're the best answer. These are your highest-probability wins.
Cover the major engines. The same question can produce different answers on ChatGPT, Gemini, Claude, and Perplexity, so a question isn't really "tracked" until you're watching it across the engines your buyers use.
That list, a few dozen real questions across those five types, across the major engines, is a far truer picture of your AI visibility than a thousand checks of your own name.
The scale problem, and the point of tracking
Here's the catch that makes this hard to do by hand. A meaningful question set is dozens of queries, times four or more engines, checked repeatedly over time because answers change. That's hundreds of checks on a recurring schedule, and AI answers are probabilistic, so a single check tells you little; you need the trend. Doing that manually is not a job anyone sustains past the first week.
That's exactly what Sourceable is built to do: track the real questions your buyers ask, across ChatGPT, Claude, Gemini, and Perplexity, on a recurring basis, so you see not just whether you're named in the category and comparison queries that matter, but how that changes as you do the work. The value of tracking the right questions only shows up when you track them consistently, and consistently is where automation beats the manual ritual.
Stop asking AI about yourself. Start asking it the questions your buyers ask, and start watching the answers.
FAQ
Isn't checking what AI says about my brand name enough?
No. People who search your exact name already know you exist. The questions that drive discovery and decisions, category, comparison, and problem queries, usually don't mention your name at all, and those are where you're won or lost.
Which question type matters most?
Category questions ("best tools for X") for most brands, because that's the core discovery moment where an undecided buyer gets a shortlist. Comparison and problem questions are close behind.
What are problem-based questions?
Queries where a buyer describes a pain without knowing the solution category or any brands, like "how do I stop losing leads." Tracking these catches buyers at the very top of the funnel, before they know what to search for.
How many questions should I track?
Not hundreds, a few dozen real ones across the five types (branded, category, comparison, use-case, problem), phrased the way your buyers actually talk, and monitored across the major engines.
Why do I need to track across multiple engines?
Because the same question can return different answers on ChatGPT, Gemini, Claude, and Perplexity. You can be named in one and absent in another, so tracking a question on a single engine gives you a partial picture.
Top comments (0)