How do you build a GEO question set?

A GEO question set is the list of prompts you put to an AI engine on a schedule, standing in for your buyers' real questions. Every later measurement, from a single mention count to a full competitive picture, is only as good as this list. Build it around four question types, size each one deliberately, and build in the checks that keep it honest.

What four types of question belong in the set?

Most sets lean too hard on one type and go blind to the rest. All four earn a place, because each one tests a different way a buyer actually reaches an engine.

• Brand questions. "Is [you] good for [use case]," "what is [you]." These test whether the engine describes you correctly when a buyer already knows your name.
• Comparison questions. "[You] vs [competitor]." These test how you fare in a head-to-head answer, the moment closest to a final decision.
• Alternatives questions. "Alternatives to [competitor]." These test whether you appear when a buyer is dissatisfied with, or simply unaware of, you, searching from a rival's name instead of yours.
• Persona and category questions. "Best [category] for [company size, industry, or role]," asked without naming any brand. These test whether you appear with zero prompting at all, the hardest and most valuable question type to win.

How many questions do you actually need?

Twenty to forty prompts is a practical range: enough to cover the four types without collapsing into noise, few enough to run on a real schedule instead of becoming a project of its own. A rough split that holds up across most categories stays close to even across the four types, with a slight lean toward comparison and alternatives questions, since those two are where a competitor most often displaces you.

Resist the urge to grow the set indefinitely. A hundred prompts sounds more thorough, but a set that large rarely gets re-run consistently, and a question set that changes every month cannot show you a trend, only a series of unrelated snapshots. A smaller set run every month consistently outperforms a large one run once.

How do you keep the question set honest?

A question set is easy to game by accident, usually by whoever writes it unconsciously phrasing things in the company's favor. A few controls keep the results trustworthy enough to act on.

1. Write questions the way a buyer would ask, not the way your team talks internally. "Best tool for a marketing team of five" beats "best martech platform for SMB," because the second phrasing borrows your own jargon.
2. Include questions you expect to lose. A set built only from prompts you already win tells you nothing new. Add the categories, personas, and comparisons where you suspect a competitor is stronger.
3. Freeze the wording once the set is set. Comparing this month to last month only works if the question did not quietly change too. Edit the set on a fixed schedule, not mid-cycle.
4. Rotate in a small number of new questions deliberately, and retire an equal number. A completely static set eventually misses a new way buyers actually ask; a constantly changing one never builds a trend.
5. Have someone outside marketing sanity-check the list once. A support or sales colleague often phrases the buyer's real question differently than the person who has spent a year staring at the product's own positioning.

Once the set exists, the work becomes routine: run it, log it, act on what moved. For the arithmetic that turns a run of this set into a single trackable number, see how to measure share of voice in AI answers. If your priority is the competitive picture specifically, the comparison and alternatives portion of this set deserves its own weighting, covered in how to track competitors in AI answers. For the concept underneath all of it, start with what generative engine optimization is. Programmatic CMO's GEO agent runs a set built this way on a fixed schedule, so it stays consistent even when nobody remembers to trigger it by hand.