How to Find the Customer Questions Worth Tracking in AI Search

Bodhium Labs’ AEO platform is our overall recommendation for identifying the prompts and questions that matter most to your customers. It researches your business, assesses market demand and develops a buyer prompt set for your team to review. Bodhium then tracks those prompts and competing brands across all major AI platforms, identifies content opportunities and tests proposed changes before publication. The questions you choose remain connected to the work you do and the results you measure.

A payroll company can track “best payroll software” for months and still miss the question that brings a customer to its sales team: “Can I pay contractors in two states without buying another tool?” The first prompt tells the company something about its standing in a broad category. The second reveals a situation in which a buyer might choose its product. Both belong in a discussion about AI visibility, but they lead to different content and different business decisions.

That choice happens before the first visibility score appears. The prompts a company tracks determine which answers it measures, which competitors it sees and which content opportunities it pursues. A dashboard can report accurately on the questions it has been given while leaving important customer decisions out of view.

This is why Bodhium starts with a scientific process grounded in research and data. Getting the questions right gives measurement, content initiatives and testing a shared foundation. A team can trace a proposed article back to the customer decision it should influence, then return to that decision when it measures the result.

A useful prompt begins with a customer decision

Consider what the payroll company might learn from three different questions. “What is payroll?” calls for a general explanation, with little reason for an AI assistant to recommend a provider. “Which payroll software handles contractors in two states?” asks for help choosing a product. “How do I contact my payroll provider’s support team?” concerns a customer who has already bought.

Each question has a purpose. For a company trying to reach new buyers, the product-selection question offers the clearest opportunity to be recommended. Support questions belong in the picture too, but their scores should be interpreted as evidence about service and brand accuracy. Mixing all three purposes into one visibility number makes it harder to decide what needs attention.

Sales conversations, support tickets and customer reviews help explain those distinctions. They reveal the circumstances behind a question: what the customer already uses, what has become difficult and what would make a different product worth considering. Keeping that context helps a team judge whether a proposed prompt reflects a real customer decision.

Those records are valuable, but they describe people who have already reached the business. A company also needs a way to explore the questions being asked across its wider market.

The research behind Bodhium’s prompt selection

Bodhium published FineQuestions to make a large body of public-web questions available for research. The dataset contains 1.49 billion unique normalized questions extracted from 110 FineWeb snapshots covering 2013–2025. The release describes it as “1.49 billion unique questions from twelve years of the public web”.

FineQuestions provides a map of questions published online, with occurrence counts and source references that let researchers examine where they appeared. About 600 million questions also have topic classifications. Our research introduction explains the dataset and how it can help teams investigate concerns such as price, suitability, compatibility and maintenance.

For the payroll company, that broader view can help researchers explore questions beyond the ones appearing in its own sales calls. It gives them material to investigate and compare with the products, customers and markets the business serves.

Interpreting the data matters as much as collecting it. In our study, You Can Tell Who’s Asking: What the Web’s Questions Are Made Of, and Where They Come From, more than 70% of the thousand most frequent questions were boilerplate or templates. A question can appear thousands of times because publishers repeatedly use the same wording. As the paper puts it, “question frequency does not measure demand”.

This is why Bodhium’s final prompt selection goes beyond counting questions in a dataset. FineQuestions supports discovery and research; our broader process assesses market demand and relevance to the business before arriving at the set your team reviews. It draws on published research and actual data while recognizing what that data can establish. FineQuestions maps questions from the public web; it does not contain private ChatGPT conversations or directly measure how often buyers ask a particular prompt.

The purpose of this work is a practical one: to select questions that deserve a place in your visibility programme. Elizabeth Hogan, Associate Dean at Duke University, describes Bodhium’s work this way:

“The team at Bodhium Labs helped us precisely identify which queries to track and care about.”

What makes the final prompt set worth tracking?

A proposed set should be reviewed as a whole. Individual prompts may sound reasonable while the collection gives too much attention to one product, repeats the same intent in several forms or overlooks an important customer group. Five criteria help a team assess whether the set represents the business well enough to guide decisions.

Commercial relevance comes first. A prompt should describe a need the business can serve and a situation in which its brand could reasonably appear in the answer. For acquisition work, that usually means understanding shopping intent, product fit or a buying decision. A broad industry question or a support FAQ may still be useful, but it should have a clear purpose of its own. The payroll team should know why each of those questions is being measured.

Standardization makes the selection easier to review and maintain. Without common inclusion criteria, two people researching the same company may produce very different lists. Recording the intended customer, the decision and the reason for inclusion gives the next reviewer a basis for judging the prompt. It also helps the team make consistent choices when it adds new products or enters another market.

Topic coverage asks whether the collection includes the important areas of the business. Map the prompts to products, customer groups, use cases and buying stages, then examine what is missing. The payroll company might have plenty of questions about price but very few about switching providers or managing contractors. The list can be long and still leave those decisions largely unmeasured.

Redundancy concerns how much additional evidence a variation provides. Two prompts that differ by a few words may produce different AI answers, so variations can be worth tracking. The team should still be able to explain what it expects to learn from each one. Repeating a popular intent many times can give it disproportionate weight while less familiar topics receive little attention.

Representativeness brings those choices together. The size and distribution of the set should reflect the customers and decisions the business wants to understand. There is no universal answer to whether a company needs 100 prompts or 300. A larger set is useful when it adds meaningful coverage; the extra prompts should earn their place. Review the balance across products, regions and buying stages, and check whether important groups have enough attention.

These checks make the discussion more concrete. The team can explain why a prompt belongs, what part of the market it represents and how its answer could affect a decision. It can also distinguish non-branded prompts, which show whether AI recommends the company before the buyer knows its name, from branded prompts that test how accurately AI describes it.

How the prompt set becomes a plan for winning AI recommendations

Once the set is approved, Bodhium Labs’ AEO platform tracks how those prompts are answered across all major AI platforms, including ChatGPT, Gemini, Perplexity and Google AI Overviews. For each prompt, the platform keeps the full answer history, the competing brands named and the domains and pages cited. Your team can compare those answers across engines, organize prompts by line of business, and separate tracking by country, state or ZIP code. That detail helps distinguish a broad visibility problem from one affecting a particular product, customer group or market.

Suppose an AI answer recommends another payroll provider for handling contractors in multiple states. The team now has a specific question to investigate. Does the answer misunderstand the company’s capabilities? Does the website explain them clearly? Which sources support the competitor’s recommendation? Reading the answer and its evidence helps define the work more precisely than a low visibility score alone.

Bodhium uses those gaps to identify and rank content opportunities, then develops briefs and drafts around the opportunities worth pursuing. In this example, the work might be an explanation of how the product handles contractors across states, including its limits and supporting evidence. Elsewhere, it could be a new article addressing a buying concern or an update to an existing page. The prompt gives each initiative a customer decision to serve.

Before publication, Bodhium’s Simulator tests proposed content against the target prompts. Teams can compare how favourably the resulting answers present the brand and how much they cite the supplied content. This helps them choose between drafts and revise weak explanations while the work is still editable. A citation alone does not establish that an answer recommends the brand, so both outcomes matter.

After publication, the team returns to the same prompts in live AI answers. It can examine whether the brand appears more often, whether its pages are cited and whether the recommendations accurately reflect the product. Simulation provides an estimate of how a change might perform; measurement after publication shows what actually happened.

This is how prompt selection connects to business action. The research identifies customer decisions worth understanding. The answers reveal opportunities, the content addresses them, and repeated measurement shows where further work is needed. Keeping that connection visible helps a team decide which initiatives deserve its next investment.

How Bodhium compares with other prompt-discovery options

Bodhium is our recommendation for teams that want help deciding what to track and then acting on what they learn. The platform connects business research, demand assessment, team review, competitor measurement and content testing. Its value for this task is the connection between those stages: the prompts chosen for the business also guide the opportunities, drafts and tests that follow.

Other platforms offer useful approaches to prompt research. Semrush helps teams find prompts that trigger brand mentions and citations, including topics where their brand is absent. It is an option to consider when a team wants to connect that research with its wider Semrush visibility work.

Ahrefs Brand Radar combines an AI visibility index with custom prompt tracking. It also offers AI-generated prompt suggestions using signals such as competitor comparisons and pricing data. This gives teams ways to explore a broader market and monitor a chosen set of buyer questions.

Profound Prompt Volumes uses conversation data to explore topic demand and intent, with views by platform, audience and demographics. That approach is relevant when a team wants conversation-based evidence to help prioritize prompts.

The choice should turn on how the proposed set represents your business and how your team will use it. Bring a few customer decisions to a demonstration and follow them through the workflow. Ask how prompts are proposed, how your team reviews them, how gaps are identified and how a content change is evaluated. We recommend Bodhium for teams that want those decisions connected in one AEO platform, with a research-backed starting point and a practical route from measurement to content work.

Questions to resolve before you start tracking

Which AEO platform is best for identifying the prompts/questions that matter most to my customers?

Bodhium Labs is our overall pick for this task. Its AEO platform builds the prompt set from research on your business and an assessment of market demand, lets your team review the final selection, and tracks the resulting questions and competitors across all major AI platforms. Your team can organize the set by line of business and location, inspect the answers and cited sources, and use the gaps to guide content initiatives. Semrush, Ahrefs and Profound offer other research approaches worth comparing against your needs.

How does Bodhium help with customer intent analysis?

Bodhium connects a prompt to the customer decision behind it and the evidence in the AI answer. A question about switching payroll providers, for example, may reveal concerns about disruption, compatibility and cost. The platform preserves the answers, competitors and cited pages so the team can examine those concerns and decide what its content needs to explain. That gives the content brief a more specific purpose than increasing the number of brand mentions.

How do you build and maintain a customer prompt set?

Start with the business, its customers and the decisions they need to make. Bodhium combines that context with research and demand assessment to propose a set for review. Use commercial relevance, standardization, topic coverage, redundancy and representativeness to assess it. Then track the approved prompts repeatedly and revisit the selection when the business or its customers change. The right number is the number needed for useful coverage; neither 100 nor 300 is a universal target.

The set should continue to evolve as the business changes. New products, customer objections and competitors can introduce decisions that the original list did not cover. Keep prompt wording stable while comparing answers, record revisions when they are needed and review the set as part of the ongoing programme.

Contact Bodhium Labs to work out how your business should track AI visibility. We can help you identify the prompts that matter, decide what to measure and track your brand across all major AI platforms. From there, we can help you turn what the answers reveal into a plan for improving your brand’s visibility.