How to find the questions your customers ask AI

Find the questions customers ask AI by collecting the language they already use: sales call notes, support tickets, onboarding forms, search suggestions and forum threads. Then rewrite each as a first-person question with the problem first and no brand name in it. A good list covers different intents, such as best, compare, price and how to, and stays fixed so you can repeat it over time.

Why do the questions matter so much?

An AI visibility measurement is only as good as its questions. If you ask questions nobody asks, you measure nothing useful. If you ask questions that name your brand, you measure whether the assistant knows you, not whether buyers discover you.

The goal is to reproduce, as closely as you can, what a buyer types before they know your name.

Where do you find real questions?

Your own conversations.

• Sales call recordings and notes: the problem statements prospects open with.
• Support tickets and chat logs: how customers describe what they are trying to do.
• Onboarding questionnaires: why they came and what they compared.
• Win and loss notes: which alternatives they considered.

These are the richest source because they use your buyers' own words.

Search and question tools.

• Search suggestions that appear as you type a query.
• "People also ask" style boxes on results pages.
• Keyword research tools, for rough demand.

Public communities.

• Forum and community threads where people ask for recommendations.
• Review site questions and comments.
• Social posts asking "does anyone know a good...".

Your competitors' audience. The questions their customers ask are often yours too.

How do you turn them into good test questions?

A few rules keep the list honest.

1. First person, problem first. "I run a small agency and need a way to report on client SEO. What should I use?" beats "best SEO reporting tool", though shorter forms are fine too since people type both.
2. No brand names, yours or anyone else's, in discovery questions. A comparison like "alternatives to [competitor]" is a distinct, labelled type of question, if you include it at all.
3. No leading words. Avoid adjectives that tilt the answer, such as "innovative" or "leading".
4. One need per question. Compound questions muddy what the answer is for.
5. Keep it natural. If a customer would not say it, do not write it.

What mix of questions should you cover?

Cover a range of intents so that you see where you win and lose.

Include broad questions, where established brands dominate, and narrow ones, where a specialist can win. Citewake's methodology uses this same logic, with broad, mid-level and specific tiers, so that a reader can see where a brand is strong.

Also note which questions are about products, where a good answer names brands, and which are purely informational, where it may name none. Both are worth knowing, but they should be counted separately. Mentioned, recommended or cited explains why counting needs that care.

How many questions do you need?

Twenty to thirty is a workable start for one business. A larger set gives a steadier and more varied picture, and makes it easier to see which kinds of questions you win. Quality and coverage matter more than raw size.

What if you have no customer data yet?

Then borrow from the market. Read the forums and communities where your category's buyers ask for advice, and note how they phrase their problems. Look at the questions that search engines suggest as you type a topic. Scan reviews of competitors for the reasons people chose them.

You can also ask a few prospective customers how they would describe their problem, without mentioning solutions. Their phrasing is worth more than any list you invent.

Use AI itself with care. Assistants can suggest question ideas, but their suggestions tend to sound tidier and more formal than real queries, and they may reflect the same patterns you are trying to test. Treat them as a source of prompts for thought, and validate each idea against real customer language before you add it to your fixed list.

How do you keep the list useful?

Freeze it. Number the questions, store the exact text and use it for every round. If you must change a question, record that and treat results before and after as not directly comparable. Add new questions as a new set rather than editing old ones. How to build a benchmark set of questions goes deeper.

Revisit the list now and then against fresh customer language, because needs change. But change deliberately, not casually, and replace guessed questions with observed ones as your own data grows.

What do you do next?

Run the list through the assistants, using a clean session and repeats, as in how to test AI answers properly. Save each answer. Then count how often you are recommended, mentioned or cited. If you would like a quick first look before building the full list, the Citewake checker is a simple way in.

What is the short version?

Mine real conversations first, then search suggestions and public communities. Rewrite what you find as first-person, problem-first questions with no brand names. Cover several intents, freeze the list, and keep adding observed language over time. The better your questions, the more your measurement reflects what buyers actually see.