Imagine a small agency choosing project management software. The shortlist looks promising: each product offers task boards, file sharing, and collaboration. Then someone asks a question the comparison pages have not answered: “Do we have to pay for every freelancer who drops in to review a task?”
The answer could change the shortlist. So could the distinction between viewing a file, leaving a comment, and editing a project. A page that says “built for collaboration” leaves the buyer to work out the part that matters.
AI prompt research starts with questions like this. It helps a marketing team identify the decisions its content needs to support, investigate how AI assistants answer those questions, and choose what to improve.
Generating possible prompts is easy. Establishing which ones reflect your audience takes research. Without that work, a team can end up tracking questions its customers rarely ask and commissioning articles that leave the real objections unanswered.
The goal is a set of buyer questions you can explain and defend: where each came from, why the answer matters, and what your brand can usefully contribute.
Key Takeaways
- Build the prompt set from identifiable evidence. Keep actual AI prompts, customer questions, Google queries, and generated ideas clearly labeled.
- Prioritize questions using product fit, customer evidence, and the importance of the buying decision. Low visibility alone does not establish demand.
- Turn validated questions into specific content actions, then monitor the same prompts over time. Track mentions, citations, and business outcomes separately.
Why Buyer Questions Need Their Own Research Process
AI assistants give people another place to ask for help before they arrive on a brand's website. In its September 2025 study of consumer ChatGPT usage, OpenAI reported that 49% of messages fell into its “Asking” category. That includes noncommercial activity, so it does not tell us how many people were shopping. It does show how central information and advice are to the way people use the service.
Website analytics captures only part of this activity. Someone can read a recommendation, reconsider an option, or leave with another question without visiting your site.
Pew Research Center's analysis of Google browsing behavior illustrates the measurement gap. Among 900 U.S. adults whose March 2025 browsing it analyzed, traditional-result clicks occurred on 8% of Google visits with an AI summary, compared with 15% without one. The study was observational, and search results were collected in April. It describes an association in that sample, rather than proving that summaries caused the difference.
For a marketer, the implication is to investigate what buyers encounter as well as what brings them to the website. That investigation depends on the questions chosen for testing. A set dominated by broad category questions will tell you little about compatibility, implementation, or pricing concerns unless those concerns are also represented.
This is part of the problem addressed by IAB's August 2026 AI visibility framework, which identifies prompt-type coverage, sample size, testing cadence, reproducibility, and platform coverage as dimensions of measurement quality. An AI visibility score becomes easier to interpret when you understand which questions produced it.
Researching those questions is therefore a prerequisite for useful measurement. It also gives the content team a clearer brief: here is the buyer's uncertainty, here is our evidence that it matters, and here is the information needed to resolve it. The research supports this approach to investigation; it does not establish a guaranteed revenue return from following a particular workflow.
What Is AI Prompt Research?
AI prompt research is the process of collecting and validating questions that represent how an audience might use AI assistants to research a topic, evaluate options, or make a purchase.
Some inputs are actual prompts shared by customers or captured within a research provider's dataset. Others are candidate prompts developed from sales conversations, support requests, search queries, and public discussions.
The result is a defined set of questions with enough supporting context to explain why each belongs in your research. That set can inform content planning and provide a basis for testing how your brand appears in AI answers.
Prompt research supplies inputs for generative engine optimization (GEO), the work of improving how a brand and its content appear in AI-generated answers. It also helps distinguish a content need from a measurement question: a customer may need a clearer answer even before you have evidence that the question is common on an AI platform.
How Prompt Research Adds to Keyword Research
Keyword research helps you understand search demand, competing pages, and the terms people use. Prompt research adds a way to examine the circumstances behind a question.
Consider these illustrative searches:
- “Project management software” identifies a category.
- “Project management software for a small agency” adds an audience.
- “Which project management tool lets freelance designers update tasks without paying for a full seat?” introduces a constraint that could change the recommendation.
The third question is useful because of the decision it describes, not because it meets a word-count requirement. Detailed queries also occur in Google Search, and AI prompts can be short.
Modern search already interprets concepts and intent. Google uses systems such as neural matching and passage ranking to understand concepts and relevant sections of a page. Preserve your keyword research and add the questions it does not resolve, such as which constraints make a buyer reject a product that otherwise fits the category.
Use keyword research to assess established search demand. Use prompt research to explore the needs, comparisons, and objections your content must address. Neither dataset provides a complete view of demand on its own.
Where to Find Questions Worth Testing
Start With Customer Conversations
Review sales calls, support tickets, onboarding questions, and on-site chat transcripts. Look for questions that ask whether a product is suitable for a particular situation.
Useful details include budget, team size, location, compatibility, implementation effort, and reasons for rejecting an alternative. Review won and lost opportunities as well as existing customers. Record the question in the buyer's words before translating it into a standardized test prompt.
Ask the teams handling these conversations: Which question repeatedly delays a decision? Which answer requires a follow-up document? Which limitation surprises prospects? The responses give the research a commercial purpose without assuming every question was asked in an AI assistant.
A support ticket is evidence of a customer need. It is not proof that the customer submitted the same question to ChatGPT. Label it accordingly.
To collect direct evidence, ask customers who used an AI assistant during their research whether they would share the relevant prompt and response. Record the engine and date, and remove personal or confidential details before sharing the example internally. One customer's conversation remains one observation.
Use Public Discussions to Expand the Research
Industry forums, Reddit, review sites, and community discussions can surface concerns your own customers have not raised. Read the replies as well as the opening question. Follow-up comments often explain why an apparently suitable option does not work.
In the agency example, a discussion titled “Affordable project management tools” might initially look like a pricing question. Further down, the author could explain that freelancers join for a week and disappear for a month. The underlying concern is paying for intermittent access. A generic article about low-cost software would only partly address it.
Keep the surrounding context with the question. Also check whether apparently repeated concerns come from independent discussions or from people repeating the same original post. Several mentions are more useful when you understand what they represent.
People Also Ask can supply additional question ideas. Treat these as research leads, not as a record of AI prompts or a reliable reconstruction of an engine's internal searches.
Review Your Google Search Queries
Google Search Console shows queries associated with your site's appearances in Google Search. Review questions, comparisons, and phrases containing constraints such as “without,” “under,” “compatible with,” or “for small teams.”
Use a custom regex query filter in the Performance report to explore phrases such as (?i)(how|which|compare|versus|alternative|without|compatible). The pattern is a starting point for review: it can produce irrelevant matches and miss useful questions.
Look at the landing page alongside the query. If someone searches for guest-access pricing and reaches a general features page, ask whether that page answers the question directly. This does not prove the visitor was dissatisfied, but it identifies a concrete piece of content to inspect.
Do not restrict the research to long queries. “Software without per-seat pricing” can reveal a useful need in only a few words. Search Console supplies Google query evidence, not a record of what those visitors asked other AI services.
Use Prompt Data Providers With Their Methodology in View
Some tools provide data drawn from observed AI conversations. Profound Prompt Volumes, for example, describes using double-opt-in consumer panels and probabilistic modeling to estimate broader patterns across ChatGPT, Gemini, Claude, and Perplexity.
Similarweb's description of its prompt research data identifies ChatGPT, Perplexity, Gemini, and Google AI Mode as sources of real-user prompts for its Prompt Analysis offering.
Before using a volume estimate to prioritize content, check its geography, period, platform coverage, and unit of measurement. Ask whether it represents an exact phrase, a group of related questions, or a modeled topic total. Record those details alongside the number.
Use compatible estimates to compare opportunities within a dataset. Do not divide prompt volume by Google keyword volume and present the result as a market-demand multiplier unless the measurement bases support that comparison. An unavailable estimate should remain “unknown,” rather than being converted to zero.
Use AI to Suggest Questions You May Have Missed
An assistant can help expand a research set. Give it a product category, a customer situation, and a specific constraint. Ask for questions someone might need answered before choosing.
Label the output as generated. Then compare it with customer evidence and product reality before adding it to your tracking set.
For example, you could ask an assistant to suggest questions an agency should consider when inviting temporary collaborators into its software. Review the suggestions against what your sales and support teams actually hear. A question about audit logs might be relevant to one customer segment and unnecessary for another.
Keep generated suggestions separate from observed questions. Google explains that AI Mode and AI Overviews may use query fan-out to search related subtopics, but asking an assistant to suggest related questions does not expose the searches it executed.
How to Validate and Prioritize Your Prompt Set
1. Choose One Buying Decision
Start with a product, audience, or use case narrow enough to research properly. “Small agencies choosing project management software for freelance collaboration” is more actionable than “business software.”
Identify what the buyer needs to decide and what could rule an option out. This gives you a basis for accepting or rejecting prompt ideas.
2. Record the Original Question and Its Source
Keep the original wording alongside the source, date, audience, and relevant product or use case. Label the evidence as an observed AI prompt, a customer question, a search query, a public discussion, or a generated suggestion.
If you create a cleaner version for testing, retain both versions. “Will we be charged for someone who only checks the artwork?” might become “Does an external reviewer need a paid seat to comment on a task?” The rewrite clarifies the activity while retaining the commercial concern.
It would be a different question if you rewrote it as “Which project management software is cheapest?” That version removes the requirement you were trying to investigate. Keeping the original beside the test prompt makes this kind of drift easier to catch.
3. Preserve Constraints That Change the Answer
Remove irrelevant details, but retain the requirements that affect suitability.
“Which tool is best for agencies?” and “Which tool lets an agency invite freelancers without buying full seats?” should not automatically become the same prompt. The second tests a specific commercial requirement.
4. Group Questions by the Decision They Address
Cluster prompts when they could reasonably be answered by the same content. Separate them when the audience, requirement, or underlying decision changes.
Useful groups might include category discovery, product comparisons, alternatives, pricing, implementation, and compatibility. Check the groups against the buying journey you are trying to support. These are planning categories, not fixed stages every buyer follows.
An implementation question could come from a prospect assessing effort or an existing customer trying to finish setup. Use the source context to interpret it.
A useful clustering check is to draft the answer in a sentence. If two questions need substantially different qualifications, do not merge them simply because they share words. Guest pricing and guest permissions may belong on the same page, but each still needs an explicit answer. Keep both questions visible in the brief.
5. Prioritize Business Relevance and Evidence
Use a simple editorial triage for each cluster. This is a proposed working method, not an industry scoring standard.
Prioritize now: The product fits the requirement, relevant customers or independent sources have raised it, and answering it would address an identifiable evaluation obstacle. Record the evidence and assign a content owner.
Research further: The question is plausible, but its importance is uncertain. Ask a customer-facing team to review it, look for corroborating discussions, or investigate compatible demand data before commissioning content.
Exclude or defer: The question duplicates an existing cluster, falls outside your market, or asks for a capability the product does not provide. A genuine product limitation should not become a content brief promising otherwise.
Recurrence helps, but it is not the only criterion. One question associated with a strategically important use case may warrant attention. State that business reason explicitly rather than implying that a single observation establishes broad demand.
The output should be a short list of questions your team can defend, with the evidence and proposed next step attached to each.
6. Establish a Repeatable Baseline
Run the selected prompts across the engines relevant to your audience. Record the response, date, engine, citations, and test conditions. Keep language, location, and session setup as consistent as the interface allows.
Review whether the brand appears, how it is described, and which sources support the answer. Repeat testing before treating a single omission as a persistent gap.
Keep tests of a standalone question separate from tests of a multi-turn conversation. If a follow-up depends on earlier context, retain that context and document the sequence. Otherwise, you are testing a different question. Use multi-engine testing to keep those observations separate by platform rather than letting one engine's answer stand in for the others.
Worked Example: From Customer Question to Content Brief
Return to the hypothetical agency evaluating freelancer access. The buyer asks, “Do we have to pay for every freelancer who reviews a task?” The sales team can answer, but the relevant details are scattered across pricing information and a permissions guide.
That is enough to investigate a content problem. It is not yet enough to claim that the prompt is popular in AI search.
Find Out What the Buyer Means by “Reviews”
Before drafting anything, clarify the activity. Does the freelancer need to view an attachment, leave feedback, change a deadline, or assign work to another person? Those actions may carry different permissions and charges.
If the original concern is occasional commenting, a brief about unlimited project collaboration would expand the promise beyond the question. The research should keep the task specific enough for the product team to verify the answer.
Test the Questions That Could Change the Choice
Develop a small group of candidate prompts around the concern:
- “Which project management tools let external reviewers comment without a full paid seat?”
- “What can a guest do compared with a paid member?”
- “When does adding an external collaborator increase the subscription cost?”
These are illustrative test prompts, not observed demand data. Each examines a different part of the decision: finding suitable options, understanding access, and anticipating cost.
Run them under documented conditions and inspect the answers. Suppose a response cites a comparison page that describes guest access but does not explain its limits. That is a source to review, alongside your own documentation. It does not establish why the engine chose the page or whether changing one sentence will alter the answer.
Give the Writer a Brief They Can Act On
The brief could ask for an update to the existing guest-access page, with a clear purpose: help an agency understand whether occasional external reviewers require paid seats.
Specify what the page must explain: who qualifies as a guest, which activities are permitted, which plans include the feature, and what triggers a charge. Ask the product owner to approve the conditions. Link the explanation to the relevant pricing and permissions documentation so a reader can check the details without starting another search.
Include exclusions too. If editing a project requires a paid seat, the page should say so. If access varies by plan, that distinction belongs beside the answer, where a buyer will see it.
The content task is now much more precise than “write an article about collaboration.” It has an audience, a question, a set of required facts, and a clear place on the site.
Check Whether the Work Resolved the Original Problem
After publication, ask the sales team whether the page answers the question they were previously explaining manually. Continue testing the same prompt cluster and record any changes in mentions, citations, or factual accuracy.
Those are separate observations. A clearer page can be useful to customers even if sampled AI answers have not changed. A rise in citations, meanwhile, does not by itself show that the page update caused the rise or produced more sales.
The example has done its job when the research leads to a specific, verifiable content improvement and a sensible way to follow it up.
Decide Whether the Gap Requires Content, Authority, or a Product Fix
A new article is one possible response. Before commissioning it, diagnose the problem:
- If the answer is missing from your site, add the information to the page where a buyer would expect it.
- If the information is outdated or contradictory, correct and consolidate it.
- If cited third-party sources misstate your offering, pursue an accurate correction with supporting evidence.
- If the product does not meet the requirement, record the limitation and decide whether it belongs in product feedback.
Google's guidance makes clear that existing SEO practices remain relevant to AI Overviews and AI Mode, with no special optimization requirement. Useful, accessible content remains the starting point; a particular format does not guarantee inclusion.
This diagnosis should feed the wider GEO execution process: choose the page or source to improve, assign an owner, verify the change, and retain the evidence needed to review it later. Turning a GEO dashboard finding into action requires that handoff. A list of missing mentions does not tell a writer what to fix.
How Pixis Visibility Helps Put the Research to Work
Research gives your team a set of questions worth investigating. Pixis Visibility helps you examine how your brand appears in the answers and connect those observations to content work.
The platform supports custom prompts alongside prompts discovered from a category and its competitors. It tracks ChatGPT, Gemini, Perplexity, and Claude, with analysis by prompt and engine. Citation, competitor, and sentiment information helps teams inspect both brand presence and how the brand is described.
For the agency example, a team could add prompts about freelancer access, compare the answers across supported engines, and review the sources cited. If the same requirement repeatedly reveals missing or unclear information, that finding can inform a content brief.
Visibility also supports briefs and drafts, connecting research to execution. Supplying brand context for AI content, including approved product facts, audience needs, and limitations, helps keep that work specific. The brief should carry the evidence from the original research into the draft, so a general product description does not replace the answer the buyer needed.
Use the platform to monitor a researched set of questions and investigate patterns. A tracked response is an observation from that test, not a transcript of every customer's AI conversation.
Measure Visibility Alongside Business Outcomes
Keep brand mentions and website citations separate. An AI answer can recommend your product while linking to an independent review, or cite your website without recommending the product.
For a consistent test set, monitor the proportion of evaluated responses that mention the brand and the proportion that cite an owned website. Segment branded prompts from category and comparison prompts so that questions naming your brand do not obscure competitive gaps.
Retain the underlying answers. Prompt-level measurement lets you inspect the questions and responses behind an aggregate rate. That matters when a brand is being mentioned more often but described as unsuitable, or when the answer repeats a feature claim that is no longer accurate.
Track identifiable AI-referred visits and conversions alongside these observations. Visibility measures presence in sampled answers; analytics measures recorded activity on your site. Neither alone explains the full buying journey.
If prompts, engines, or test conditions change, document the change before comparing periods. A larger prompt portfolio can change the reported rate even when performance on the original questions has not moved.
Review research quality as well as visibility. Are priority buyer decisions represented? Can the team trace each prompt to its source? Have generated suggestions been reviewed? Has the work produced an approved content action? These checks tell you whether the research is usable before any ranking or citation result arrives.
Frequently Asked Questions
Can I Find the Exact Prompts My Buyers Use?
You can obtain specific examples when customers share their conversations. Research providers may also offer observed prompt data within their datasets. Sales calls, Google queries, and AI-generated suggestions help develop candidate prompts, but they do not establish what an individual buyer submitted to an assistant.
How Many Prompts Should I Track?
There is no universal count. Begin with a manageable set covering your priority audience, use cases, comparisons, and purchase constraints. Expand when an important decision is missing. Treat a small initial set as exploratory rather than representative of the entire market.
Do I Need a Separate Page for Every Prompt?
No. Related prompts may belong to one page if they address the same decision. Create separate content when the audience, requirements, or answer differs enough to justify it.
Does Every Prompt Need Search-Volume Data?
No. A recurring customer question can justify useful content even when a keyword tool reports little volume. Record the evidence behind the decision instead of treating an unavailable estimate as zero demand.
How Often Should I Review the Prompt Set?
Use a regular review schedule and revisit the set when products, pricing, markets, or recurring customer questions change. Preserve a consistent core for comparisons, and document additions or revisions.
Build the Research Around a Decision You Can Improve
The value of prompt research is the connection between a buyer's question and a useful action. You should be able to explain where the question came from, why it matters, and what information would help answer it.
Start with one buying decision. Gather the evidence, test the relevant questions, and address the gaps your team can substantiate. Then use Pixis Visibility to follow how your brand appears across the supported engines as that work develops.