---
title: "Find the Buyer Prompts AI Answers Without You"
description: "Build an AI prompt-gap map from converting search terms and Search Console queries, then test where AI answers recommend competitors instead of you."
url: "https://groas.com/post/how-to-find-the-buyer-questions-ai-answe"
image: "https://pub-87da24ecbbfc4c3bad6875f3aa013712.r2.dev/generated-images/30ef0008-0e3a-4c32-b469-90c7a92ade4f.png"
published: "2026-10-09T05:36:14.553Z"
modified: "2026-10-09T05:36:14.619Z"
---

October 9, 2026 · 10 min read

# Find the Buyer Prompts AI Answers Without You

[Alexander PerelmanHead Of Product @ groas](https://groas.com/author/alexander-perelman)[LinkedIn](https://www.linkedin.com/in/alexander-433793253/)

![Tiny figures walk a giant unrolled receipt of search terms on a desk, planting red flags where buyer questions are answered without the brand.](https://pub-87da24ecbbfc4c3bad6875f3aa013712.r2.dev/generated-images/30ef0008-0e3a-4c32-b469-90c7a92ade4f.png)

In this article

1. [Why not brainstorm the prompt list first?](#why-not-brainstorm-the-prompt-list-first)
2. [Step 1: Where do I find questions buyers actually ask?](#step-1-where-do-i-find-questions-buyers-actually-ask)
3. [Step 2: How do I turn a search term into a fair prompt?](#step-2-how-do-i-turn-a-search-term-into-a-fair-prompt)
4. [Step 3: How do I test the prompts and record the answers?](#step-3-how-do-i-test-the-prompts-and-record-the-answers)
5. [Step 4: What kind of gap am I looking at?](#step-4-what-kind-of-gap-am-i-looking-at)
6. [Step 5: Which gaps should I fix first?](#step-5-which-gaps-should-i-fix-first)
7. [Do I need an AI visibility tool to do this?](#do-i-need-an-ai-visibility-tool-to-do-this)
8. [What should I do today? Build a 20-prompt starter map](#what-should-i-do-today-build-a-20-prompt-starter-map)

Your best AI prompt list is probably sitting in your Google Ads search terms report. Most teams ignore it, brainstorm questions that sound good in a positioning deck, and then wonder why their visibility audit tells them nothing about buyers.

Five people in a room can produce thirty polished prompts by lunch: “What is the best enterprise billing platform?” “How do modern logistics fleets manage driver dispatch?” They sound plausible because they repeat the language the company uses about itself. Buyers bring messier questions: what a tool costs, whether it connects to the system they already use, and what breaks if they switch.

**Start with the questions attached to commercial intent**, not the questions your team wishes people asked. Your paid search terms, Google Search Console queries, and sales-call objections give you a prompt list grounded in buyer language. This guide shows you how to turn that list into neutral prompts, test where AI answers leave you out, and sort the gaps into fixes. The result should be a short punch list, not another dashboard to admire during a monthly meeting.

## Why not brainstorm the prompt list first?

A whiteboard session tends to produce questions that flatter your positioning. Test “What are the top three platforms for B2B pipeline acceleration?” and you may get a tidy list of big names. You have learned little about whether an answer engine understands the constraints that make someone buy from you.

![A polished boardroom whiteboard beside a hopper overflowing with raw search-query receipts.](https://pub-87da24ecbbfc4c3bad6875f3aa013712.r2.dev/generated-images/7b0e06ff-ac03-4f15-a0ca-ea50fbc3ce55.png)

Real buyers search with friction: “does platform X require developer hours to connect to Salesforce” or “cheapest fleet dispatch software for ten drivers.” The integration, team size, and price concern are not incidental details. **They are the buying question.** Strip them out and you test a broad category summary instead of a shortlist decision.

I would still keep a few broad prompts to see how an engine describes the category. I would not let them define the audit. If you want to know where a competitor wins a buyer’s attention, start with the searches that already brought buyers to your door.

## Step 1: Where do I find questions buyers actually ask?

Begin with the data you paid to collect. Pull your Google Ads search terms report for the past 12 to 18 months, filter for terms with at least one conversion, and export them. One conversion does not make a term a guaranteed winner, so keep the conversion count and cost beside each query. You will need them when it is time to prioritize.

Then work through three sources in order:

1. **Search terms:** Separate vendor evaluations and operational questions from navigational traffic. You can [apply regex filters for commercial intent](https://agencyanalytics.com/blog/regex-for-seo), using patterns such as `(best|vs|review|comparison|alternatives)` and question starters such as `^(who|what|where|when|why|how|which|can|is|does|are)\b`. Read the results; a filter is a sorting aid, not a substitute for judgment.
2. **Search Console queries:** Look for question-shaped searches where your domain gets impressions but little response. As a working cut, inspect queries with more than 100 impressions, click-through rates under 3%, and average positions between 8 and 20. Those thresholds give you a manageable review pile, not a verdict on every query outside it.
3. **Sales-call objections:** Add the constraints prospects say aloud in your notes or Gong or Chorus transcripts. “We evaluated Vendor A, but their seat pricing penalizes non-admin users” tells you more about a shortlist decision than another brainstormed “best platform” prompt.

That gives you an inventory of buyer language with different kinds of evidence attached. Paid conversions tell you where someone took an action; Search Console shows where you have visibility but may not be answering the question; sales calls supply the details that short search queries often leave out. **Keep those signals distinct.** Do not pretend an impression is a sale or that every converted search term belongs in a prompt test.

## Step 2: How do I turn a search term into a fair prompt?

A search term such as `fleet management software pricing per truck` is useful evidence, but it is a fragment. A buyer asking a conversational engine for a recommendation may explain the team, the budget, and the tool they need to replace. To test that situation, expand the fragment without writing a question designed to force your brand into the answer.

![Diagram of the three-part prompt formula: role context, operational constraint, and comparison anchor.](https://pub-87da24ecbbfc4c3bad6875f3aa013712.r2.dev/generated-images/d17d6330-76b7-4958-abb8-d2d962ba2945.png)

I use three parts:

- **Role context:** Who is buying, and for what size operation? For example: “We are a 12-technician residential HVAC contractor.”
- **Hard constraints:** Which integration, compliance requirement, or billing limit matters? For example: “We need QuickBooks sync under $400 a month without per-technician license markups.”
- **Comparison anchor:** What incumbent or problem are they trying to escape? For example: “We are looking to replace ServiceTitan.”

Put them together as a plain request for options. [Phrase the prompt as a neutral buyer inquiry](https://cogny.com/blog/ai-visibility-chatgpt-claude-gemini-perplexity), not “Why should we pick \[YourBrand]?” The latter mainly tests whether an engine can talk about a company you handed it. **A neutral prompt tests whether your brand earns a place on the shortlist without being named first.**

Do not embellish the prompt until it becomes a fictional procurement novel. Use the constraints you found in searches and sales conversations. If your source query says only `commercial hvac software`, keep it broad or pair it with a documented objection; do not invent a budget and then treat the result as proof of what those searchers wanted.

## Step 3: How do I test the prompts and record the answers?

Run each prompt across ChatGPT, Perplexity, Gemini, and Google AI Overviews. Avoid relying on your everyday signed-in session: prior activity and personalization can complicate what you see. Use fresh signed-out private sessions where available, and run each prompt at least three times in separate sessions. Answers can vary. One appearance is not a stable win, and one omission is not a crisis.

I covered the differences between engines in [Google AI Overviews vs ChatGPT vs Perplexity](https://groas.com/post/google-ai-overviews-vs-chatgpt-vs-perple). For this audit, the practical point is simpler: **log each engine separately.** A citation in Perplexity does not tell you what Gemini or Google AI Overviews will show.

For each run, record four things in a spreadsheet:

- **Mention status:** Is your brand cited with a link, named without one, or absent?
- **Mention position:** If the answer lists vendors, where do you appear?
- **Competitors named:** Who appears instead, and how does the answer describe them?
- **Cited domains:** Does the answer link to vendor sites, reviews, directories, or community discussions?

Keep the prompt text, engine, and test date with those fields so you can compare like with like later. Do not reduce the result to a single “visibility score.” Being named in plain text, cited as a source, and recommended above a competitor are different outcomes.

The cited domains deserve particular attention. A competitor recommendation does not necessarily mean that competitor’s landing page beat yours. The answer may rely on a review, directory listing, or discussion you have never considered part of your search presence. [Ahrefs research on Google AI Overviews](https://ahrefs.com/blog/most-cited-domains-ai-overviews/) found that about 37.9% of cited URLs ranked in the top 10 standard organic results. Your rank tracker alone will not show you every source worth inspecting.

**Read what the engine cited before you order a rewrite.** The source list is your first clue about whether the problem lives on your site or somewhere else.

## Step 4: What kind of gap am I looking at?

Once you have a set of omissions, resist the familiar order: “Write twenty 2,000-word posts.” Some gaps need a page. Others need a clearer answer on a page you already have. A third group cannot be fixed by editing your site alone.

![Cutaway showing three possible AI citation gaps: a missing page, a buried answer, and a third-party discussion.](https://pub-87da24ecbbfc4c3bad6875f3aa013712.r2.dev/generated-images/b8b7f7d1-afea-4eb0-8d69-cc06df4a183c.png)

Classify each prompt before assigning work:

- **Missing page:** The buyer asks a concrete question and your site does not answer it. If multi-location inventory syncing affects the choice and you never explain your relevant workflow, an engine has no direct answer from you to retrieve. Publish a concise, factual page or section that addresses the question. Do not stretch an unrelated blog post around it.
- **Uncitable page:** You cover the topic, but the answer is hard to find or use. It may be buried below corporate throat-clearing, presented unclearly, or caught in a rendering problem. [Practitioners discussing AEO tools](https://www.reddit.com/r/GEO_optimization/comments/1tp69ay/what_kind_of_tools_does_everyone_use_for_geo_aeo/) point to issues such as schema, JavaScript rendering, and answer structure. Check the page before commissioning another one. Put the direct answer near the relevant heading, then give readers the detail they need to trust it.
- **Third-party authority gap:** The engine cites independent sources where a competitor is discussed and you are absent. A [study of sources cited by AI search engines](https://searchengineland.com/ai-search-engines-cite-reddit-youtube-and-linkedin-most-study-473138) highlights how prominent domains such as Reddit, YouTube, and LinkedIn can be in those answers. If the cited material is a comparison or community discussion, another paragraph on your homepage will not create a third-party mention.

These are **diagnoses, not guarantees about how an engine works internally**. A prompt can have more than one gap, and a cited source does not explain every reason the engine chose its answer. Still, the categories prevent a costly mistake: treating every omission as a request for more content.

For a missing page, write the answer. For a hard-to-use page, fix the answer you have. For a third-party gap, identify the sources buyers and engines already consult, rather than disguising another on-site rewrite as a reputation strategy.

## Step 5: Which gaps should I fix first?

Prompt frequency inside closed conversational products is hard to measure from the outside. That makes an invented “AI search volume” estimate a poor way to choose your next project. Broad keyword volume is tempting too, but it can pull your team toward informational terms while the shortlist questions wait.

Instead, **rank gaps by the commercial evidence you already have**. Start with converting paid search terms. Look at conversion rate, cost per acquisition, and the quality of the action those terms produced. Use Search Console impressions to spot opportunities your site already touches, and use sales-call objections to identify constraints that repeatedly shape decisions.

Say a query in your paid account shows a 12% conversion rate and a $45 CPA over the last six months. If its buyer question produces competitor recommendations and leaves you out, it deserves a close look even if a keyword tool estimates only ten searches a month. The paid result does not prove the same prompt drives the same behavior inside an AI engine. It does tell you that the underlying question is worth more attention than a broad term chosen for its impressive-looking volume.

Skip the giant prompt library for now. A smaller list tied to buying decisions gives you something you can test, classify, and act on.

## Do I need an AI visibility tool to do this?

Not to build the first map. Prompt trackers such as Profound, Otterly, and Peec AI can monitor appearances, but **tracking is not remediation**. A dashboard can show that Gemini named a competitor on twelve prompts. It cannot, by that fact alone, tell your team which page to change or supply the independent discussion your brand lacks.

That is the same operating problem I keep seeing in search marketing: someone buys another report, then pays people to translate it into a list of tasks for the next meeting. If you have meaningful paid and organic search complexity, [groas](https://groas.com/) offers a different model: autonomous execution across paid and organic search, with a human strategist setting direction and guardrails. The useful distinction is execution, not a prettier chart. You still need to identify the cited-source problem accurately; no tool can turn a missing third-party footprint into genuine independent authority by editing your landing page.

If you are just starting, skip the software purchase. Prove that your prompt list contains commercial questions and that the answers reveal actionable gaps. Then decide how much continuous monitoring and execution the work warrants.

## What should I do today? Build a 20-prompt starter map

Open your ad account and export converting search terms from the last year. Pull in the relevant Search Console queries and objections from sales calls. From those sources, write **twenty neutral, constraint-heavy prompts** across four commercial categories:

- **5 replacement and migration prompts:** “How difficult is it to migrate from \[Competitor A] to an alternative if our database uses \[specific software]?”
- **5 hard-constraint prompts:** “What platforms support \[specific requirement] for a company with \[specific seat count or volume]?”
- **5 commercial-pricing prompts:** “Which tools in \[niche] offer flat-rate pricing instead of charging per user seat or percentage of spend?”
- **5 feature-gap prompts:** “Does \[Competitor B] support \[specific workflow], or are there dedicated platforms that handle it natively?”

Test them across ChatGPT, Perplexity, Gemini, and Google AI Overviews in fresh sessions where available. Log your mentions, the competitors named, and the sources cited. Then label each absence: missing page, hard-to-use page, or third-party gap.

Your first job is not to guess what buyers might ask an AI engine. **Export the questions they have already asked you.**

## Frequently Asked Questions

### Why shouldn't I just brainstorm my AI visibility prompts in a team meeting?

A whiteboard session tends to produce questions that repeat the language your company uses about itself. Real buyers search with friction such as integrations, team size, and price, and those constraints are the buying question. Strip them out and you test a broad category summary instead of a shortlist decision.

### Where do I find the questions buyers actually ask?

Pull your Google Ads search terms report for the past 12 to 18 months, filtered for terms with at least one conversion. Then review Search Console queries with over 100 impressions, click-through rates under 3%, and positions between 8 and 20, plus objections prospects raise on sales calls in notes or Gong or Chorus transcripts.

### How do I turn a short search term into a fair AI prompt?

Expand the fragment into a neutral buyer request with three parts: role context (who is buying and for what size operation), hard constraints (integration, compliance, or billing limits), and a comparison anchor (the incumbent they want to escape). Do not invent budgets or constraints your source data does not support.

### Should I ask the AI engine why someone should pick my brand?

No. A prompt like that mainly tests whether an engine can talk about a company you handed it. A neutral prompt instead tests whether your brand earns a place on the shortlist without being named first.

### How should I test my prompts against AI engines?

Run each prompt across ChatGPT, Perplexity, Gemini, and Google AI Overviews at least three times in separate fresh signed-out private sessions, since prior activity and personalization can affect results. Log each engine separately, recording mention status, mention position, competitors named, and cited domains.

### The AI answer recommends a competitor instead of me—does that mean their landing page beat mine?

Not necessarily. The answer may rely on a review, directory listing, or community discussion you never considered part of your search presence. Ahrefs research found about 37.9% of URLs cited in Google AI Overviews ranked in the top 10 standard organic results, so read what the engine cited before ordering a rewrite.

### What are the different kinds of AI visibility gaps?

A missing page means your site never answers a concrete buyer question. An uncitable page means you cover the topic but the answer is buried, unclear, or caught in a rendering problem. A third-party authority gap means the engine cites independent sources like Reddit, YouTube, or LinkedIn discussions where a competitor appears and you do not.

### Which prompt gaps should I fix first?

Rank gaps by the commercial evidence you already have: start with converting paid search terms, looking at conversion rate, cost per acquisition, and action quality. A query with a 12% conversion rate and a $45 CPA deserves a close look even if a keyword tool estimates only ten searches a month.

## Pay For Results, Not For Hours

Businesses buy the outcome, agencies resell it, and groas answers for it either way.

[See If You Qualify](https://groas.typeform.com/to/xC1bQNUT)

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