--- language: en title: "How to choose questions for AI brand monitoring | Tropk" description: "Build discovery, comparison, and buying questions to track your brand across AI platforms. See examples and a template for organizing intent, criteria, and sources." canonical: "https://tropk.ai/en/blog/how-to-choose-questions-to-monitor-your-brand-in-ai-responses" last_modified: "2026-10-12T16:05:33.458Z" --- # How to choose questions for AI brand monitoring | Tropk Choose discovery, comparison, and purchase questions that help you understand how your brand appears in AI responses. See examples and a worksheet for organizing intent, criteria, and what to observe. [Luiz Gardelin]() Published on 2026-10-03T08:18:35.421Z Categorias: AI monitoring Choose questions that represent real decisions your audience needs to make: finding a solution, comparing options, and clearing up questions before buying. Organize each question by what you want to observe and record its source. This way, [question monitoring](/en/product/monitoring) can help you decide what information to investigate or improve. Consider a company that offers customer service software for online stores in Brazil. This hypothetical example will show how to build a revisable list of questions, with intent, purchase criteria, and a reason for tracking each one. ## Key points - Choose questions tied to real audience decisions and a specific offering, with a clear reason for tracking them. - Record the source of each question: an observed question, a public source, or a team hypothesis that still needs validation. - Include country, channels, language, and other criteria only when they are part of the purchasing situation. - Separate open-ended discovery questions from comparisons and questions that already contain brand names. - Keep a baseline set of questions and review conditions for comparing periods; changes to the list can affect the aggregate result. ## Start with the decision the buyer needs to make Before writing [prompts](/en/resources/glossary/prompt), choose an offering and a purchasing situation. “Customer service software” is still broad. “An online store needs to bring WhatsApp and email conversations together for a team of six agents” already tells you the problem, who will use the solution, and a requirement that could affect the choice. Write this situation in one sentence. Then answer: does the buyer want to discover tools, check an integration, compare costs, or reduce implementation work? Each of these questions deserves its own prompt when it leads to a different decision. Also define what you will observe in the response. Does the brand appear as an option? Does the text accurately describe an integration? Is there a link to a page that supports the information? This goal prevents you from tracking questions that produce interesting responses but do not help anyone decide what to do. ## Gather questions with a recognizable source Start with questions that already reach the company. Sales conversations, support tickets, authorized interviews, and questions submitted through the website can help you find the buyer’s language. Record the question without names, personal data, or private details about the person who asked it. [Search Console](/en/resources/glossary/google-search-console) queries, questions found in search results, and public discussions can broaden the list. Use them to identify topics and criteria. A Google query can inform a question for AI, but its search volume does not tell you how often that prompt is used in [ChatGPT](/en/resources/glossary/chatgpt). AI can also suggest questions. Treat these suggestions as candidates: check whether they match the offering, whether someone in the audience would ask them, and what evidence supports including them. Generating a long list does not prove that buyers use those questions. For each candidate, record a simple source: “recurring question in sales conversations,” “query observed in Search Console,” or “team hypothesis to validate.” This distinction lets you review the list without treating a hypothesis as proven demand. ## Write the context that really affects the choice A good prompt includes the need and relevant criteria. In the example, country, customer service channels, support language, and implementation effort can affect whether a tool is suitable. Add these details when they are part of the buyer’s situation. Compare the two formulations: > What is the best customer service software? > What software brings WhatsApp and email together for an online store in Brazil with six agents and support in Portuguese? The second question makes the criteria visible. You can assess whether the response explains why an option suits the situation. It also makes the interpretation more specific: the result for this question does not represent the entire customer service software category. Do not add restrictions just to favor your brand. A budget, integration, or technical requirement needs to make sense for the audience. When there is not yet evidence, record the criterion as a hypothesis and seek to confirm it. ## Separate discovery, comparison, and questions about the brand The set can include different intents. Separate them to understand what each response shows. Below are examples from the same purchasing situation, with no required proportion between the groups. ### Discover options “What software brings WhatsApp and email together for an online store in Brazil with six agents and support in Portuguese?” Observe which brands appear as options and which criteria justify the choice. The question does not mention your company: it helps you investigate whether the brand enters the conversation while the buyer is still looking for solutions. ### Compare ways to solve the problem “For an online store with six agents, when is it worth using a shared inbox, and when is it worth adopting a customer service platform?” Observe how the response explains cost, organization, and the need for features. Here, the contribution may be an explanation of the category. The brand’s absence alone does not indicate that there is a problem. ### Check a purchase requirement “What should I check about WhatsApp integration before hiring customer service software for an online store?” Observe which requirements and sources are presented. This question can help identify information your product page needs to explain, even when the response does not recommend any tools. ### Compare familiar alternatives “How can I compare Brand A and Brand B for handling orders through WhatsApp and email at an online store?” Replace the example names with alternatives that buyers actually consider. Record that the question contains brand names. Observe the criteria and facts used in the comparison; the names are already prompted by the question itself. ### Check how your brand is described “Can Brand A bring WhatsApp and email together? What should I confirm before hiring it?” Use the company’s real name when applying the question. Compare the description with current, verifiable information about the offering. This interpretation answers a question about a known brand, so it should be kept separate from discovery questions. ### Understand an objection “How can I switch from email support to a shared platform without losing conversation history?” Observe the steps, risks, and information missing to make a decision. This question may reveal a need for documentation or an explanation of implementation. Turning it into “what is the best tool” would change the original question. ## Choose questions that provide different information Review the list by intent. If two questions repeat the same need and criteria using different words, choose the clearer formulation to start with. Keep variations when they represent a difference you want to investigate, such as another market, channel, or purchase requirement. Prioritize questions related to the current offering and decisions the team can support. A recurring question about migrating conversation history may deserve attention before a broad topic with little connection to the product. Priorities should have a recorded reason, even when there are no volume data. There is no universal number of prompts that is right for every company. Start with a set you can review, read, and connect to actions. Expand it when you identify an important purchasing situation that is missing. Hundreds of nearly identical questions do not replace this coverage. ## Create a worksheet for each question You can organize the set in a spreadsheet. Each row should make it possible to identify the question and understand why it was chosen. Use the fields below: - **Question:** exact text to be tracked. - **Intent:** discovery, comparison, requirement, brand description, or objection. - **Offering and audience:** which product or service suits the situation and who needs to make the decision. - **Criteria:** country, channels, language, budget, or other conditions in the question. - **Source:** conversation, observed query, public source, or hypothesis to validate. - **What to observe:** recommended brands, description of a feature, criteria, or cited links. - **Next decision:** what information to check, page to review, or question to investigate. Here is a completed worksheet for one of the example questions: - **Question:** “What should I check about WhatsApp integration before hiring customer service software for an online store?” - **Intent:** purchase requirement. - **Offering and audience:** customer service software; the person responsible for customer service at an online store. - **Criteria:** WhatsApp integration and use for the store’s customer service. - **Source:** team hypothesis, to validate in conversations with buyers. - **What to observe:** requirements presented and sources cited. - **Next decision:** check whether the integration page explains these requirements with current information. This worksheet connects the question to marketing work and makes clear what still needs validation. When reading a response, distinguish between **mention**, when the name appears; **recommendation**, when the brand is suggested as an option; and [**citation**](/en/resources/glossary/ai-citation), when there is an identifiable reference. A response may cite your site without recommending your product, or mention the brand without providing a source. See [how to distinguish these events](/en/faq/what-is-the-difference-between-a-mention-recommendation-and-citation-in-ai). ## Review the list without losing comparability Before starting, confirm that each question is clear, that the criteria fit the buyer’s situation, and that there is a reason to track it. Separate queries that contain brand names from open-ended queries. Note the date, tool or interface, and conditions used for the review. During tracking, keep the questions that will serve as a reference. When you add or remove a query, record the change. A change in the set can affect the aggregate result: comparing two averages from different lists requires taking that change into account. [AI responses](/en/resources/glossary/ai-response) can vary from one reading to another. A single response can help identify something to check; by itself, it does not show a trend or the effect of a website change. Review the response, criteria, and sources before turning the signal into a recommendation. The set is ready to start when each question represents an identifiable need, has a recorded source, and points to a possible decision. Use the worksheet to review what is missing. Then tracking will have a clear purpose: show what is worth checking and what action makes sense for the company. Use this analysis to [organize marketing priorities](/en/product/strategy). ## Sources for further reading - [Peec AI: organizing questions by intent and context]() - [Peec AI: set selections and comparisons over time]() - [AirOps: buying questions and company-owned sources]() ## Frequently asked questions ### How do you choose questions to monitor a brand in AI responses? To choose questions for AI monitoring, start with an offering and the decisions your buyers need to make. Gather questions observed in sales conversations, support, and public sources, add relevant purchase criteria, and record what to observe in the response. Each question should represent an identifiable need and help determine what information to check or improve. ### Where should the questions used for AI brand monitoring come from? Monitoring questions can come from sales inquiries, support tickets, authorized interviews, Search Console queries, and public discussions. Record the source without names, personal data, or private customer details. AI suggestions and team hypotheses can expand the set, but should be labeled as candidates to validate, not treated as proven demand. ### Can questions that include the brand name be mixed with open-ended discovery questions? Questions that include brand names should be analyzed separately from open-ended discovery questions. Including a brand in the prompt leads the model to mention it and helps assess how it describes the brand or handles a specific comparison. A question without company names lets you investigate which options the assistant presents spontaneously in that buying situation. The two groups serve different objectives. ### How many questions does a company need to monitor in AI responses? There is no universal number of questions that suits every company. Start with a set the team can review, read, and connect to marketing decisions. Cover different needs and buying criteria, avoiding variations that merely repeat the same intent. Expand the set when you find a relevant situation that is not yet covered. ### What information should accompany each AI monitoring question? Each AI monitoring question should include its exact wording, intent, offering and audience, buying criteria, source, events to observe, and a possible next decision. Also record the date, platform or interface, and conditions for the review. A record with this information makes it possible to review the set and distinguish a proven question from a hypothesis that still needs validation. ### How can you compare AI monitoring results without losing a point of reference between periods? To compare results across periods, keep a baseline set of questions and equivalent execution conditions. Record any queries added, removed, or changed, because changing the set can affect the aggregate metric. AI responses can vary; check the responses, criteria, and sources before treating a single reading as a trend or as the effect of a site change. ## About the author [Luiz Gardelin]() Luiz Gardelin is the founder of Tropk.ai and works in GEO/AEO, product vision, strategy, and growth, helping brands understand and improve their presence in AI responses. --- [All pages in Markdown]()