How to Find the Prompts Your Buyers Actually Ask AI Engines

To find the prompts your buyers actually ask AI engines, you have to reconstruct them from where buyers already reveal their questions — sales calls, support tickets, Reddit threads, People Also Ask, and competitor comparisons — because no keyword-volume tool exists for AI prompts the way it does for search queries. Nobody can tell you the monthly “search volume” of “best privacy-friendly analytics for startups” typed into ChatGPT. So AI prompt research isn’t about pulling volume data; it’s about mining the questions your buyers demonstrably ask and organizing them into a prompt set you can track.

This is the foundational step of Generative Engine Optimization: before you can measure or improve your presence in AI answers, you need the right prompts to measure it against. Track the wrong prompts and even a perfect measurement system tells you nothing useful.

Why there’s no keyword tool for prompts

Traditional SEO has a mature ecosystem for demand research: keyword planners, volume estimates, difficulty scores, SERP features. That entire apparatus depends on search engines exposing aggregate query data. AI engines expose none of it. There is no public dataset of “how many people asked Perplexity about X this month,” and the conversational, long-form nature of prompts means the same intent gets phrased a hundred different ways.

That sounds like a problem. It’s actually a reframing: since you can’t rank prompts by volume, you rank them by evidence that a real buyer asked them and intent to buy. A prompt that came verbatim out of a sales call from a qualified prospect is worth more than any volume estimate, because you know a buyer with money asked it. Prompt research is qualitative demand discovery, and the raw material is everywhere your buyers already talk.

Six sources for real buyer prompts

Work these sources in roughly descending order of intent. The earlier ones surface fewer, higher-value prompts; the later ones surface breadth.

1. Sales-call and demo questions

The single richest source. Every discovery call, demo, and objection is a buyer telling you, in their own words, what they need to know before buying. “How do you compare to [competitor]?” “Can you do X without Y?” “Is this overkill for a team our size?” These are prompts. Buyers who used to email these questions to a sales rep now often type them into an AI engine first — and if the AI answers without you, the call never happens. Pull recurring questions from call notes, recordings, and your sales team’s memory.

2. Support tickets and help-desk searches

Existing customers ask “how do I…” and “does it support…” questions that map directly to how-to and capability prompts. These matter because AI engines are increasingly where people go for exactly this kind of task-oriented question — and because a prospect evaluating you asks the same capability questions your customers do. Your ticket queue and help-center search logs are a free, high-signal prompt corpus.

3. People Also Ask and search autocomplete

Google’s “People Also Ask” boxes and search autocomplete surface real question phrasings for your category head terms. They’re not AI prompts, but they’re the closest public proxy for how people phrase questions in natural language, and there’s heavy overlap: a question popular enough to appear in PAA is a question people also ask AI. Start from your category head terms and harvest the question variants.

4. Reddit and community threads

Reddit is where buyers ask peers for recommendations candidly — “what does everyone use for X?”, “is Y worth it?”, “alternatives to Z that don’t cost a fortune?”. These threads are a goldmine of real buyer language, and they matter doubly in GEO: engines frequently cite Reddit and community discussions as sources, so the questions asked there both model buyer prompts and shape the answers. Search your category subreddits and niche communities for recommendation and comparison threads.

5. Competitor comparison and review pages

The existence of a “[Competitor] vs [Competitor]” page or a G2 category grid is proof that comparison prompts have demand. Review-site category pages, comparison blogs, and “best X” roundups are effectively a published map of the comparisons buyers make — and, again, the exact pages engines retrieve to answer those prompts. Mine them for the head-to-head matchups and category lists worth tracking. (Review sites shape AI answers enough to deserve their own attention.)

6. Your own analytics and site search

On-site search queries, the pages that convert, and the questions in your existing content’s engagement all reveal what buyers want answered. If you run web analytics, your search box and top-converting content are a direct line to buyer intent in your buyers’ own words.

Organize prompts into a taxonomy

A pile of a hundred prompts is unusable. Sort them into a small taxonomy so you can ensure coverage across intent types and read your results by category, not one prompt at a time. Four buckets cover most B2B categories:

flowchart TD
A["Buyer prompts"] --> B["Category<br/>'best X for Y'"]
A --> C["Comparison<br/>'X vs Y'"]
A --> D["Alternatives<br/>'alternatives to X'"]
A --> E["How-to / capability<br/>'how do I do Z'"]

Category and how-to prompts win reach and awareness; comparison and alternatives prompts win high-intent buyers who are close to a decision. A healthy prompt set spans all four — if you only track category prompts, you’ll miss the buyers deepest in the funnel. Comparison prompts are consequential enough that they get their own playbook.

Turning prompts into a tracked set

Once you have your prompts sorted, the goal is a stable set you run repeatedly, because AI visibility is measured as a trend across sampled runs — and a trend only holds if the prompts stay comparable from run to run. (Answers are non-deterministic, so a single run of a single prompt tells you almost nothing; see why you can’t trust a single AI answer.)

Two practical constraints shape the set:

Prioritize ruthlessly, because prompts are limited. In Kitbase AI Visibility, the number of active prompts per project is plan-limited on Cloud — Starter 5, Pro 10, Business 25, and Premium 50 — so you can’t track everything. That limit is a feature: it forces you to pick the prompts with the clearest buyer intent rather than diluting attention across a hundred low-signal queries. Deactivated prompts keep their history and stop counting against the limit, so you can rotate seasonal or campaign prompts in and out without losing data.

Let the tool suggest prompts too. You won’t think of every phrasing your buyers use. Kitbase can suggest prompts for your project, giving you candidates to seed or extend your set beyond what you brainstormed. Combine machine suggestions with the human sources above — sales calls and support tickets surface the high-intent questions a generic suggestion never would, while suggestions catch category-standard phrasings you might miss.

Add your prompts in bulk (one per line), keep the set stable, and let the daily runs build your presence trend. When you later discover a better prompt, swap it in deliberately and note the change — comparability is the thing you’re protecting.

A starter workflow

1. List your category head terms — the two or three ways buyers describe what you do.
2. Pull 10–20 real questions from sales calls and support tickets, verbatim.
3. Harvest question phrasings from People Also Ask and Reddit for your head terms.
4. List your live comparisons — every “vs” and “alternatives to” that applies to you.
5. Sort into the four buckets and check you have coverage of each.
6. Cut to your plan’s prompt limit, keeping the highest-intent prompts, and add tool suggestions for phrasings you missed.
7. Track the set daily and read the presence trend by category.

The output isn’t a one-time list — it’s a living prompt set you revisit as you learn which questions actually drive buyers to your category.

FAQ

Is there a keyword-volume tool for AI prompts? No. AI engines don’t expose aggregate query data the way search engines do, so there’s no public “prompt volume” metric. Instead of ranking prompts by volume, rank them by evidence that a real buyer asked them — sales calls, support tickets, and community threads are your best sources.

What do buyers actually ask ChatGPT before buying? The same questions they used to ask sales reps and search engines: category questions (“best X for Y”), head-to-head comparisons (“X vs Y”), alternatives (“alternatives to Z”), and capability or how-to questions. The exact phrasings live in your call notes, support tickets, and category communities.

How many prompts should I track? As many high-intent prompts as your plan allows, prioritized ruthlessly — Kitbase Cloud plans range from 5 to 50 active prompts. It’s better to track a focused set of prompts your buyers genuinely ask than a large set of low-signal queries, because a smaller comparable set produces a cleaner trend.

Where do AI engines get the answers to these prompts? From a mix of what the model learned in training and what it retrieves in a live web search — often review sites, comparison pages, Reddit threads, and editorial roundups. That’s why the same sources you mine for prompt research are also the pages that shape the answers.

How often should I update my prompt set? Keep the core set stable so your trend stays comparable, and change it deliberately — swapping a prompt in or out when you learn something, and noting the change. Rotate seasonal or campaign prompts using deactivation, which preserves their history without counting against your limit.

Not sure which prompts your buyers ask AI? Start your free trial — 7 days, no credit card required — add your prompts, and see exactly how the engines answer them.