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Updated 2026-07-10

How to find the buyer prompts your customers ask AI

The first two articles in this series covered the strategic shift from SEO to GEO and how to write passages AI engines can cite. This is the measurement half of the system: deciding which questions are important enough to monitor.

That choice controls every result downstream. Mention rate, citation rate, sentiment, and competitor share of voice are all calculated against the prompts you selected. If the set is mostly branded questions, you will look visible because the buyer already supplied your name. If it is full of broad educational questions, you may collect interesting answers that have no connection to a buying decision. The useful middle is a stable set of questions where the answer can change a shortlist.

What is a buyer prompt?

A buyer prompt is a natural question a plausible customer asks an AI assistant while moving from a problem to a decision. It is worth monitoring when appearing, disappearing, or being described incorrectly could change who gets considered.

That definition excludes a lot of tempting noise:

• "Tell me about Acme" is a brand lookup. It matters for accuracy, but the user already knows Acme.
• "What is marketing?" is educational, but too broad to reveal category competition.
• "Best AI visibility tool for a three-person SEO agency" is a buyer prompt. The engine must interpret a use case, compare options, and make a shortlist.
• "How can I prove ChatGPT mentions improved after a content update?" is also a buyer prompt. It exposes a concrete job, a measurement need, and the criteria the buyer will use.

The difference from a search keyword is context. A keyword compresses intent into a phrase such as AI visibility tool. A prompt usually supplies a role, constraint, comparison, or desired outcome. That extra context changes the answer and often changes which brands appear.

Start with evidence, not a blank spreadsheet

Do not brainstorm fifty prompts from inside the marketing team. Start with places where buyers already reveal their language.

1. Sales calls and lost-deal notes

Pull the questions asked before a buyer accepted a demo, requested security details, compared a competitor, or decided not to proceed. Preserve the buyer's wording. "Can this report work across five client brands?" is better evidence than your internal label "multi-tenant reporting."

Look especially for repeated constraints: team size, market, budget, integration, reporting requirement, or risk. Those details turn a generic category query into a realistic prompt.

2. Support, chat, and site-search language

Support tickets reveal implementation and trust questions. Live chat and internal site search reveal the words visitors use when navigation and marketing copy have not already supplied the answer. These sources are strongest for validation and objection prompts: pricing, data handling, setup, migration, reliability, and proof.

3. Search Console queries

Google Search Console's Performance report shows the search terms that led people to your site. The query data is incomplete — Google omits anonymized queries and stores only the most important rows — but it is still useful first-party evidence of the language people use around your category.

Treat search queries as raw material, not AI prompts. Expand a short query into the decision context behind it:

• AI brand monitoring → "How do I monitor whether ChatGPT recommends my brand?"
• AI visibility pricing → "What does AI visibility monitoring cost for one brand?"
• GEO tools agency → "Which GEO tools support reporting across multiple SEO clients?"

4. Reviews and community discussions

Reviews reveal comparison criteria buyers use after experiencing a product. Community threads reveal the language people use before vendors shape the conversation. Extract recurring jobs and objections, not isolated complaints. One dramatic comment is an anecdote; the same concern appearing across sales, support, and reviews is a strong prompt candidate.

5. AI-generated suggestions — last, not first

AI can expand a known topic into candidate questions, but it cannot prove that your buyers ask them. Use generated suggestions to widen coverage after you have supplied the audience, market, competitors, and seed topic. Then review each suggestion against real evidence. Generation is a drafting step, not demand research.

Map prompts to five buying moments

A balanced prompt set follows the decision, not the funnel labels in your CRM. Most high-value questions fall into five moments:

1. Problem recognition — "How can I tell whether AI assistants mention my company?"
2. Category discovery — "What are the best AI visibility tools for a B2B SaaS team?"
3. Comparison — "Which AI visibility platform is better for an agency managing several brands?"
4. Fit and validation — "Does this tool track citations separately from brand mentions?"
5. Risk and implementation — "How often should we run the same prompts to get a reliable trend?"

Problem prompts reveal whether your category is part of the answer. Discovery prompts reveal whether you enter the shortlist. Comparison prompts show who wins head-to-head. Validation and risk prompts reveal the facts an engine uses to justify or reject a recommendation.

If your set covers only one moment, the resulting metric is easy to misread. Ten variations of "best tool" measure one narrow surface ten times; they do not equal ten distinct buyer decisions.

Turn vague topics into monitorable questions

A monitorable prompt needs one intent and enough context to produce a meaningful answer. Add only constraints that a real buyer would know and care about.

Too broad: "Best CRM"

Monitorable: "What is the best CRM for a 20-person agency that needs client reporting?"

Too branded: "Why is Acme the best CRM?"

Monitorable: "Which CRM is best for a 20-person agency: Acme, Beta, or another option?"

Too artificial: "Provide a comprehensive analysis of leading AI visibility solutions across all enterprise criteria."

Monitorable: "Which AI visibility tool can track five client brands and export reports?"

Three editing rules keep the set useful:

• One prompt, one decision. Do not combine pricing, security, integrations, and competitors in the same question.
• Use buyer vocabulary. Prefer the words found in calls and queries over internal feature names.
• Keep constraints consequential. Add a market, role, use case, or budget only when it could change the recommendation.

Score candidates before they consume a slot

Use a simple 0–2 score across five questions. This is an editorial filter, not a scientific demand model.

1. Decision proximity: Could this answer change a shortlist or purchase requirement?
2. Brand openness: Can the engine choose among brands, rather than repeat a brand named by the user?
3. Answer consequence: Would your presence, absence, or incorrect description matter?
4. Repeatability: Will this question remain useful across repeated monitoring runs?
5. Diagnostic value: If the result changes, will you know what content, proof, or product fact to investigate?

Keep the highest-scoring prompts, then check coverage across the five buying moments. A prompt can score well and still be redundant if three others measure the same decision.

For example, "best AI visibility tools for small SEO agencies" scores strongly because it is category-open, commercially close, repeatable, and actionable. "Tell me everything about AEO Mantis" is useful for brand accuracy but weak for competitive visibility. "What is AI?" is repeatable and open, but too far from the category decision to justify a scarce monitoring slot.

Build a five-prompt starter set

The Free plan includes 5 monitored ChatGPT prompts. Use those five to cover decisions, not wording variants:

1. One problem prompt that describes the job before naming the category.
2. One category-discovery prompt with the most important audience or use-case constraint.
3. One comparison prompt using real alternatives buyers consider.
4. One fit prompt about the capability that most often decides the sale.
5. One risk prompt about trust, implementation, or proof.

Keep branded questions as a separate diagnostic layer when possible. They answer "Does the engine describe us correctly?" Category-open prompts answer the harder question: "Would the engine choose us without being told our name?" AEO Mantis marks branded prompts as metric-excluded by default so they can be monitored without inflating Mention Rate, Share of Voice, Citation Rate, or Sentiment.

When you add more slots, expand coverage before depth. Add a second use case, a genuine competitor comparison, or a market-specific version before adding another synonym for a prompt you already monitor.

Localize intent, not just words

A translated prompt is not automatically a local prompt. Buyers in different markets may use different category terms, name different competitors, care about different proof, or ask the same question at a different level of formality.

Preserve the decision while rewriting the phrasing for the market. Keep each geography and language as its own prompt so the answer remains interpretable. If one prompt lists the US, Taiwan, Japan, and Germany together, you cannot tell which market caused the result.

Run equivalent high-value prompts across markets when comparison matters, but read each locale as its own measurement series. A healthy English average can hide a market where the brand is absent or described incorrectly.

Generate candidates inside AEO Mantis

If your plan includes prompt generation, open Prompts, choose Generate from topic, enter a concrete topic, select the language, and review the suggestions before adding them. The generator uses the topic, selected language, product context, and target geography to produce up to 10 brand-blind candidates. Topic and locale are saved with the prompts you select.

Do not add the full generated batch automatically. Reject placeholders, duplicate intents, invented product assumptions, and questions no buyer evidence supports. The human decision is the final quality gate.

Keep the panel stable enough to learn

Prompt monitoring is a time series. If you replace half the set every week, a movement in Mention Rate may reflect the new questions rather than a real change in visibility.

Review the set when the product, category, competitors, or target market changes, and do a structured review at least quarterly. Remove prompts that no longer map to a buyer decision. Add prompts for new use cases and competitors. Record the old wording before materially changing a prompt so you do not compare two different questions as if they were one continuous measure.

The goal is not to discover a perfect permanent list. It is to maintain a small, defensible panel that changes slowly enough to reveal a trend and quickly enough to keep representing the market.