7 Ways to Find Out What Your Buyers Are Asking AI Search (And How to Become the Answer)

Your buyers aren’t just Googling anymore. They’re asking AI.

Real questions like:

These aren’t keywords; rather, they’re prompts with intent.

Whatever your buyers get back from ChatGPT, Claude, Perplexity, or Google AI Mode decides your fate.

Either you get recommended to potential customers, or you don’t. There’s no page two to fall back on. The good news is you can figure out what people are asking and learn how to consistently show up where it matters.

That’s what this article is about.

7 Smart Ways to Discover What Buyers Are Asking AI

AI visibility scores only tell part of the story. They swing wildly because GenAI results are probabilistic, i.e., run the same prompt twice, get different answers. That volatility makes it impossible to build a strategy off of rankings alone.

Here’s an example:

The real signal comes from the prompts themselves.

Prompt intelligence is the new keyword research.

If you know what buyers are actually asking—not the hypothetical personas you sketched last year, but the real questions showing up in ChatGPT, Claude, or other AI chatbots and search tools—you can start shaping content that sticks.

Some methods are scrappy and free. Others scale your prompt intelligence across markets, use cases, and funnel stages.

Quick View: 7 Ways to Discover Buyer Prompts in AI Search

1. Mine Your Sales and Support Teams

Your sales and support teams hear the raw, unfiltered questions your buyers actually ask. The phrasing may be awkward and the logic might be messy, but that’s the point.

Sales hears questions from people making decisions. Support hears from those already living with them. Together, they cover both ends of the funnel.

Ask your reps:

• What do buyers say right before they ghost you?
• What do they ask when they’re comparing us to someone else?
• What objections come up again and again, especially from high-fit leads?

Now flip to support:

• What are the first five questions new customers ask in month one?
• What confused them after they bought?
• What did they expect that wasn’t there?

You’ll get phrases like:

• “Does this integrate with X, or would I need Zapier?”
• “Do I need a developer to set this up or can I do it myself?”
• “How does this handle users in multiple time zones?”

They look like FAQs, but they’re also prompts your future buyers are typing into AI search tools.

To make this actionable, set up a shared doc or Slack thread. Invite sales and support to drop in actual phrases they’ve heard–verbatim, no paraphrasing. Run it for 3-5 weeks, then scan for patterns.

Look for prompts starting with “How do I…” or “What happens if…” or “Is this better than…”? Those are your starting points for TOFU, MOFU, and BOFU content. They’re also the exact kinds of questions LLMs are designed to answer first.

2. Prompt ChatGPT, Claude, and Perplexity Directly

Few marketers use AI tools to reverse-engineer what their buyers are asking. Fewer still are doing it to surface repeatable patterns.

This method is simple but surprisingly effective. Treat the AI tool like a mirror for buyer intent. Not a replacement for research, but a stand-in for the questions your audience is already asking in private.

Start broad:

Or:

With this, you observe what the AI learned about what your audience cares about, what tradeoffs it lists, product features it highlights, and blind spots it fills in.

Those are the angles your content should hit if you want to be referenced in the answer next time.

Go deeper with personas:

You’ll get something closer to stream-of-consciousness — flawed and realistic.

Tighten the signal:

1. Gather the LinkedIn URLs of 3-5 people in your ICP (ideally, folks who are active).
2. Ask AI to analyze who they are, what they post about, and what seems to drive their mindset.
3. Use SparkToro to see what content they like and share. This shows where LLMs might draw supporting context.
4. Combine these inputs and you get both the micro-context (what specific people care about) and the macro-context (what the industry is talking about).
5. With that foundation, ask ChatGPT to suggest MoFU and BoFU content topics. This is especially useful when exploring a new territory or vertical.

What you’re doing here is stress-testing your messaging against the AI’s perception of your category. You’re also uncovering the prompt structures that drive recommendations, comparisons, and shortlists.

Create a table: prompts on one side, key considerations the AI surfaced on the other. Use this to plan content.

3. Analyze Conversational Datasets with Profound

There’s only so much you can learn by testing prompts one at a time. Eventually, you need to zoom out and see patterns: what people ask, how often, in what tone, and at what buying stage. That’s what tools like Profound help you do.

Profound’s Conversation Explorer gives you access to a constantly updating dataset of real AI-human interactions. These are the questions people ask across tools like ChatGPT, Gemini, and Claude, aggregated, categorized, and mapped by intent.

Say you’re marketing billing software for freelancers. Search “invoicing tools” or “Stripe alternatives” and you’ll see real queries like:

They show up in clusters—sometimes dozens or hundreds of times—tagged by sentiment and purchase intent. You’re no longer guessing what to write. You’re seeing what problems keep surfacing, how buyers are framing them, and which competitors are mentioned alongside yours.

Profound connects the dots, surfacing patterns impossible to spot manually:

• Which use cases are gaining traction (e.g., “freelancers with clients in multiple currencies”)
• What language people use to express dissatisfaction with a tool (e.g., “overkill,” “confusing pricing,” “clunky UI”)
• Which questions show up mid-funnel vs right before purchase

That level of signal is gold for mapping your AISO content roadmap. You’re getting ahead of buying behavior and building assets that speak directly to real prompts.

If you don’t have access to Profound, this is one worth demoing, especially if you’re running a content operation that spans multiple ICPs or geographies. The more varied your customer base, the more likely individual prompt tests will miss the full picture.

4. Audit Your Brand’s LLM Presence with Chosenly

Buyers trust what AI tells them. If ChatGPT says your product does X, they believe it. Even if that information is wrong, outdated, or pulled from a two-year-old blog post you forgot about.

Chosenly audits how your brand shows up in GenAI answers. It tracks your presence across AI tools and then shows what’s being said, how often, and where that information is coming from.

The key difference: Chosenly runs each prompt multiple times. That matters. AI answers shift constantly–one run tells you nothing, but ten or twenty give you signal.

Say you offer a freemium scheduling tool. Chosenly shows you appear in 3 out of 10 prompts for “best scheduling tools for solopreneurs,” but only 1 of 10 when the prompt mentions a budget constraint. It also reveals that ChatGPT is citing a 2021 roundup post listing features that no longer exist in your free plan.

Now you know two things:

1. You’re inconsistently visible.
2. You’re being misrepresented by outdated sources.

Chosenly also flags the specific pages AI pulls from and suggests citation improvements.

You also see competitors—maybe a rival shows up in every “privacy-compliant” prompt, telling you where you’re losing narrative control.

5. Leverage Traditional Search Data with AnswerThePublic

Many AI prompts are built on the same bones as traditional search queries. That’s where AnswerThePublic can still be useful. It surfaces autocomplete data from search engines, i.e., questions, comparisons, and modifiers people are already typing.

When someone asks an LLM, “What’s the best accounting tool for self-employed consultants?”, that structure likely traces back to common search behavior. AnswerThePublic shows you how people phrase those early questions and what language they use to frame tradeoffs.

Try this: plug in your product category. If you’re marketing a wireless mouse, search that. The tool will map out clusters like:

These are real prompts in disguise. Use this data to shape your top-of-funnel content. Even if these searches don’t get clicks in Google, they can still show up in GenAI responses. And because AnswerThePublic groups related questions, you can use them to form the basis of FAQ pages, use-case explainers, or “best for” product comparisons that actually mirror buyer language.

6. Reverse-Engineer Query Fan-Outs with Qforia

When someone asks ChatGPT a question, the answer doesn’t come from that prompt alone. LLMs run a process called “query fan-out.” One prompt turns into dozens of micro-queries behind the scenes. Qforia helps you see those.

This matters because the AI doesn’t just ask “best analytics tools for ecommerce.” It fans that out into sub-questions like:

Your content needs to answer these, even if they’re never asked out loud.

Qforia simulates how Google’s AI Mode performs fan-out. It takes your target prompt, generates the hidden sub-queries, and shows you the reasoning paths the AI might be using to build its answer. It also classifies these queries by type: comparative, implicit, recent, reformulated, and entity-specific.

If you’re working on a feature page, a comparison table, or a buyer’s guide, this tool shows you what supporting content needs to exist not just on the page, but often in specific passages. You’ll know which questions your content should answer at the paragraph level to show up in GenAI lists or side panels.

Say you sell invoicing software. Qforia takes “best invoicing platforms for international freelancers” and shows the AI likely expands it into:

Now you can check: do your pages answer these? If not, fix the gap. And if your competitor does, that’s the version GenAI will recommend.

Qforia is more technical than other tools—you’ll need a Gemini API key—but worth it for critical conversion pages.

This is how you stop writing for one query and start writing for the invisible ones, too.

7. Monitor Brand Mentions in AI with Peec

Getting mentioned in AI answers is good. Knowing how often, where, and in what context? That’s better. That’s what Peec helps you track.

Peec is built for brands that want visibility in AI-powered search to be measurable, not anecdotal. It monitors how frequently your company appears in AI responses across tools like ChatGPT, Perplexity, Gemini, and others. More importantly, it tells you what prompts are surfacing your brand, whether you’re being cited positively or neutrally, and which sources AI models are pulling from.

Say you’re marketing a subscription analytics tool. Peec shows you’re consistently mentioned when users ask about “revenue dashboards for DTC brands”, but are completely absent from prompts about “tools with built-in churn prediction.” That’s a signal. You might offer the feature, but if it isn’t being associated with you in trusted sources, GenAI can’t connect the dots.

Peec tracks competitors too. You can see who’s being recommended for which types of queries, how sentiment shifts over time, and where your narrative is falling behind.

What sets Peec apart is that it doesn’t just look at your brand. It also surfaces the web content AI is using as scaffolding. That could be your homepage, a review on G2, a quote in a Reddit thread, or a tutorial someone posted three years ago.

If your visibility hinges on outdated or weak sources, Peec shows you what to replace or who to email.

Use it to spot content gaps, track perception drift, and prioritize updates based on what GenAI is learning from.

What to Do With These Buyer Prompts Once You Find Them

Every prompt you collect is a signal. It tells you something about who the buyer is, what they’re trying to solve, and how they’re thinking about the decision. And if you can map those prompts to content that answers the question well, in a format LLMs can parse and cite, you start showing up more often, and more consistently.

The goal isn’t just to match prompts one-to-one. It’s to build clusters. Think of each cluster as a constellation of related buyer questions around a core topic or job-to-be-done. One prompt might focus on ease of use, another on pricing, another on integrations, but they’re all orbiting the same decision point.

For example:

That’s a cluster. You’ve got implicit questions about:

• Currency support
• Tax handling by country
• Language localization
• Customer support across time zones
• Pricing models that scale with usage

If your content only answers one of those, you’re less likely to get picked up by GenAI.

This is where structured, passage-optimized content comes in. Think feature pages with bullet-point clarity. Comparison pages that make tradeoffs explicit. FAQs that group common questions by persona or pain point. Use-case stories that reflect the same phrasing and constraints your buyers describe.

And it’s not just what you write, it’s where and how. If an outdated G2 review is the top source cited in answers about your pricing, and it’s wrong, you need a plan to fix that. If your integration docs are locked behind login walls, GenAI may never see them. If your strongest differentiator lives on a generic product page with no markup or examples, it may never surface in an answer.

You’re not writing for Google anymore. You’re writing for an AI model that reconstructs understanding from fragments. Every paragraph, sentence, and table is either helping or hurting your visibility.

Start by taking a single prompt cluster and mapping it to content assets. What exists? What’s missing? What needs restructuring?

Then ask: Would this answer show up in ChatGPT if someone pasted it as-is?

That’s your bar.

Going From Prompt Intel to Content Map (With Examples)

When collecting prompts, you’ll notice patterns. Some repeat with slight variations. Others change based on role, budget, or use case.

Turn that noise into content by organizing your prompts into themes and then mapping each theme to a piece (or type) of content you can either create or improve:

Some of these can be full-blown landing pages. Others might be better suited as blog posts, feature pages, or even support docs.

But the common thread: your content shouldn’t just explain what your product does. It should reflect how buyers talk and the exact phrasing that shows up in prompts.

When GenAI decides which brand to recommend, it looks for alignment in features, clarity, structure, and context. The better your content matches the language of the buyer prompt, the more likely it is to be cited.

Why Accuracy Matters More Than Visibility in AI Search

Showing up in AI answers is only a win if what’s being said is right.

Plenty of brands get mentioned, but the AI can hallucinate or get the facts wrong. A pricing tier that no longer exists, a feature you’ve retired, or a positioning claim you’ve never made — and because the response sounds confident and includes citations, readers believe it.

The worst part is you might never know unless you’re looking for it.

That’s why tracking visibility alone isn’t enough. You also need to monitor brand accuracy: what’s being said, which sources are influencing that narrative, and how it changes over time.

Look closely at the answers. What features are listed? What use cases are emphasized? Which reviews or blog posts are cited? If anything’s wrong, trace the source. If it’s your site, fix the page. If it’s a review platform, update your listing. If it’s a blog post, consider outreach or publish a better version on a more authoritative site.

You can do this at scale with the tools we mentioned earlier. They flag misstatements, show which pages are influencing AI answers, and help you prioritize fixes that are most likely to shift the model’s output.

And when a source can’t be changed—say it’s a third-party blog from 2019—flood it out. Publish three new pages that get the facts right:

• One on your site
• One on a platform GenAI tools trust (Reddit, LinkedIn)
• One on a third-party publisher (via press release, partner post, etc.)

The AI may not “believe” you immediately, but repeated, high-authority citations start to tilt the model’s output.

Key Takeaways for AI Search Leaders

Prompt intelligence is essentially the new keyword research.

The brands that win in GenAI are building a consistent presence across the prompts their buyers actually use. That means publishing structured, buyer-aligned content that’s easy for AI to cite, and keeping a close eye on how your brand narrative shifts over time.

And it all starts with knowing what buyers are asking their AI search tools.

Prompt Intelligence FAQs

What is AI search visibility? And how should I measure it accurately?

AI search visibility is how often your brand shows up in AI answers for given prompts. Some tools simplify this into “visibility scores,” but most come from single-prompt runs and can mislead you.

AI answers are probabilistic. Ask the same question twice, get different results. In one multi-month study, 70-90% of citation domains changed between January and July. That volatility means one-off scores are no better than a coin toss.

The better approach is to:

• Run prompts multiple times and average the results
• Track patterns over time, not snapshots
• Pair visibility tracking with accuracy checks (is the information about you even correct?)

Visibility without stability or accuracy won’t help you shape strategy.

What are the different types of questions buyers are asking in AI search?

Buyer questions fall into clear categories. Traditionally, you had short, keyword-style queries (“Best CRM for freelancers”) and slightly longer comparison questions (“HubSpot vs Mailchimp for small teams”).

But AI introduces generative queries—richer, multi-constraint prompts bundling context, needs, and tradeoffs:

For example:

You can use a tool like Profound to adapt to this shift. Old-style queries were short and generic. New ones are longer, layered with buyer context, and harder to predict, but they’re far more useful for shaping content strategy.

Do users browse citation links in AI Mode and AIO answers?

Not often. In Google’s AI Mode, 91% of citations appear in the side block, not inline. That means users can skim the full AI-generated answer without ever clicking through.

But don’t mistake low click-throughs for irrelevance. Citation visibility drives consideration, not just traffic. If your brand name shows up in the block—even without a click—you’ve planted it in the buyer’s mental shortlist. Many of those impressions surface later as branded searches or direct signups.

What content do LLMs prefer?

LLMs surface complete, well-structured answers. They reward content anticipating not just the main query but hidden follow-ups AI generates (“query fan-out”).

For example, the user prompt might be:

The AI silently expands it into:

If your page only answers the first question, it’s less likely to be cited. If you cover the full set with clear subheads, tables, and FAQs, you’re far more AI-friendly.

LLMs also tend to prefer:

• FAQs, comparison tables, and pricing breakdowns
• Clear headers with semantic structure
• Entity-rich language (product names, features, industries)
• Support docs, reviews, and authoritative third-party content

How can you use buyer questions to shape your content strategy?

Treat prompt insights as a content ideation and roadmap inspiration.

Yes, informational searches deliver less traffic than before. But they’re still cited by AI, linked by other blogs, and scaffold answers. Writing credible informational content keeps your brand seeded across the web.

For bottom-of-funnel prompts (e.g., “Best CRMs for startups under 10k MRR”), build:

• Listicles with clear comparisons
• Supporting FAQs that expand on decision criteria

For job-to-be-done / daily-job prompts, flip perspective:

• What jobs are your buyers trying to accomplish?
• How does your product fit into their routine?

That becomes your blueprint for:

• “Day in the life” blog posts or social series
• Discussion starters on Reddit and niche forums
• Repurposable snippets for LinkedIn or newsletters

The more you reuse these insights across platforms, the more your AI visibility compounds.