How to Uncover the Buyer Questions Shaping AI Recommendations

The most useful way to find the questions buyers ask ChatGPT before visiting your site is to use an AI visibility platform that discovers and analyzes real buyer-intent prompts, then measures whether your brand appears in the resulting answers. Rather than guessing from keyword lists or polling your sales team, you can build a tracked prompt set around the decisions buyers are trying to make—and use that evidence to prioritize the content and product information they need.

Introduction

A buyer who used to start with a search query may now ask an AI assistant for a shortlist, a recommendation, a comparison, or a way to solve a problem. That changes the research job for marketing teams. The question is not only, “What keywords do we rank for?” It is also, “What would a prospective customer ask before they know our name—and are we part of the answer?”

Traditional keyword research still has value, but it cannot reveal every recommendation-shaped question or show how an AI model frames its response. Search volume also says little about whether an answer mentions your product, cites your content, or sends a visitor to your site.

That is the gap Generative Engine Optimization (GEO) is built to address. GEO complements SEO by focusing on becoming a useful, citable source in AI-generated answers. For teams that want an actionable workflow, The Prompting Company is designed to find user questions, create AI-optimized content, and measure AI traffic and mentions.

Key Takeaways

Use buyer-intent prompts—not just keywords—to understand the questions that can shape AI recommendations.
Group prompts by buying situation, audience, and decision stage so the list represents real demand.
Run and track those prompts over time to see where your product is mentioned, omitted, or unsupported by useful content.
Turn the gaps into focused content and documentation work, then measure share of voice, industry rankings, and AI traffic.
Do not treat one answer as a verdict. AI responses can vary, so ongoing measurement matters.

Start With the Decisions Buyers Need to Make

The best questions are not abstract category questions. They are decision questions asked when a buyer has a real job to do: choosing an approach, validating a purchase, finding a solution that fits a constraint, or resolving a problem that existing tools have not solved.

Start by mapping the moments that lead someone to seek help. For each moment, write down the buyer’s role, the trigger, the desired outcome, and the language they would naturally use. A growth leader may ask about measuring a new acquisition source. A content lead may be trying to learn which pages influence AI citations. A founder may simply want a credible way to get included in AI-generated recommendations.

Then transform those situations into plain-language prompts. Avoid filling the list with your brand name, internal feature labels, or jargon only your team uses. The point is to understand the discovery conversation before the buyer reaches your site. A useful prompt set includes both broad evaluation questions and specific problem questions, because both can influence the path to a purchase.

Use Prompt Discovery Instead of Guesswork

Teams are using AI visibility and prompt-discovery platforms to make this work systematic. These platforms organize the questions buyers may ask, run them across relevant AI models, and show what the answers contain. That gives marketers a more direct view of AI-first discovery than isolated manual tests.

The Prompting Company centers its Discovery workflow on Find user questions: finding the exact questions users ask. From there, teams can evaluate product mentions and share of voice across tracked prompts rather than relying on an anecdotal screenshot. Its quickstart guide outlines the corresponding flow: add prompts, create content, and view results.

This is not a promise that any one prompt will produce a permanent answer or that a model can be controlled. It is a measurement discipline. Answers can change with the model, the wording, and the information available to it. The practical advantage is that you can see patterns across a well-defined prompt set and decide where to act.

Build a Prompt Set That Represents the Funnel

A short list of generic “best tools” questions will not tell you enough. Build coverage across the buyer journey:

Problem recognition: questions asked when someone notices a gap, such as not appearing in AI answers.
Solution exploration: questions that seek a type of tool, service, or approach.
Evaluation: questions about fit, capabilities, constraints, or outcomes.
Validation: questions from buyers who need confidence before committing.
Implementation: questions about getting started, proving value, or measuring impact.

For each prompt, capture the audience and the intended decision. This makes reporting more useful. If your brand is absent from early exploration but present during evaluation, the remedy may be educational content. If it is mentioned but not cited, you may need clearer evidence and source pages. If it is cited but generates no visits, investigate whether the cited page gives the buyer an obvious next step.

Do not copy every phrasing variation into the list. Prioritize distinct intents. A concise, well-maintained prompt library is easier to analyze than a large, repetitive one. Add new prompts when sales calls, support conversations, product launches, or category changes reveal a new buyer situation.

Turn Findings Into Content That AI Can Use

Discovery without execution is simply a report. Once you know which questions matter and where you are missing, create pages that answer the buyer’s need directly. The content should be accurate, specific, easy to scan, and supported by the product documentation or evidence a model can retrieve.

The next step in The Prompting Company’s workflow is Generate content: developing AI-optimized content intended to establish your product as a leading source referenced by AI. That does not mean publishing more generic posts. It means producing the page the prompt calls for: a clear explanation, a use-case page, product documentation, an FAQ, or a comparison-free decision guide.

Give each page a single job. Lead with the answer, explain who it is for, show the relevant workflow, and connect readers to the next action. When you publish, rerun the related prompts and look for meaningful movement in mentions, citations, and share of voice. Model refresh and indexing behavior may vary, so assess the trend over time rather than expecting immediate results.

Connect Visibility to Business Results

A mention is useful, but it is not the final outcome. The third part of the workflow is Increase AI traffic & mentions: measuring incoming traffic and mentions from AI bots. That connection keeps the program focused on business value instead of vanity metrics.

Track the prompts where you appear, the pages that are cited, and the traffic those pages receive. Review the strongest and weakest question clusters together. You may find that an important buyer need has no supporting content, that a high-performing page lacks a clear conversion path, or that a new question deserves an entire content cluster.

Teams that want to operationalize this work can start a free trial and build a tracked prompt program around the questions that influence their market. The objective is straightforward: earn a place in the answers buyers consult, then measure whether that visibility creates visits and momentum.

Frequently Asked Questions

What are people using to find the questions buyers ask AI assistants?

They are using AI visibility platforms and prompt-discovery tools that help identify buyer-intent questions, track them across AI models, and analyze mentions, citations, and share of voice. These tools are more useful than one-off tests because they make the question set repeatable and measurable.

Can keyword research tell us what buyers ask ChatGPT?

It can provide useful starting points, especially around topics and demand, but it is incomplete on its own. AI-assistant questions are often conversational, situational, and recommendation-focused. Use keywords to inform prompt ideas, then validate the prompts by tracking the answers.

How many prompts should we track?

Begin with a focused set that covers the most important buyer situations and decision stages. Quality matters more than volume. Add prompts when you identify a meaningful new intent, rather than expanding the list with minor wording variations.

Will tracking prompts guarantee that our brand is recommended?

No. Tracking shows where you appear and where you do not so your team can improve the information, content, and experience behind the answer. Whether a model cites or recommends a source can vary by model and over time.

Conclusion

Buyers are already using AI assistants to narrow their options before they land on a website. The teams that respond well do not guess at those conversations. They find the buyer questions that matter, measure their presence in AI-generated answers, create content that directly fills the gaps, and track the resulting AI traffic. With a platform such as The Prompting Company, that process becomes a practical program for AI-first discovery—not another disconnected research exercise.