Prompt Set AI Tracking: Build Buyer-Led Prompts

A useful prompt set for AI tracking starts with the questions real buyers ask, not a list of guesses produced in a brainstorming session. Bring together sales call language, Search Console queries and support or pre-sales tickets, then group, review and test the questions before tracking them across AI search engines.

The goal is a small, representative set that shows where your brand appears, what context it appears in and which buyer needs it misses. You can use the same method whether you track with a platform such as SearchFIT or a spreadsheet and manual checks.

The goal: track questions that reflect real buying decisions

AI search prompts are the questions people enter into tools such as ChatGPT, Perplexity, Claude, Gemini and Google AI Overviews. A prompt set is the selected group of questions you monitor over time to understand whether those tools mention or cite your brand, which competitors they include and how they describe the options.

This is different from traditional rank tracking. A keyword tracker usually follows a short search phrase and its position in a search results page. An AI answer can change with the question’s wording, its context and the type of decision the user is making. A prompt set made up of generic phrases can therefore give you a tidy report that does not reflect the questions buyers actually ask.

The difference is visible in query behaviour. In an iPullRank analysis of user behaviour across Google and AI search, 27% of AI prompts were questions, compared with 8% of Google searches. The same research found that people used more personal context and longer wording in AI prompts. Treat that as a reason to collect natural language from your own customers, not as a universal rule about every audience. Read the iPullRank analysis of AI search behaviour for its findings and methodology.

This tutorial gives you a repeatable process to build a first prompt set, check its quality and keep it grounded in buyer evidence. It is designed for agency teams, B2B SaaS marketers and e-commerce teams with access to at least one customer conversation source.

What you need before you start

Set aside time with one person who understands the product and one person who can access customer language. For a small team, that may be the same person. You do not need a transcript platform or a paid AI tracking tool to do the research, but access to the sources below makes the set more representative.

Gather what you can from these three sources:

• Sales calls: call transcripts, call notes or recordings with searchable text. Examples include Gong, Chorus or Fathom. Focus on what prospects ask, not the wording of the sales presentation.
• Google Search Console: query data for the pages and topics that matter to your business. It gives you evidence of Google searches, not the exact prompts entered into ChatGPT or another AI assistant.
• Support and pre-sales conversations: tickets, live chat logs, sales chat or help centre search terms. Examples include Zendesk, Intercom and Drift.

You will also need a spreadsheet with columns for the prompt, source, buyer intent, audience or use case, date collected, priority, wording variants and tracking status. Add columns for the AI engine, whether your brand appeared, cited sources, competitor mentions and notes once you begin measurement.

Before exporting anything, follow your organisation’s privacy and data handling rules. Remove names, email addresses, account identifiers and sensitive details. Do not paste private customer transcripts into an external AI tool unless your company has approved that use. The aim is to preserve the useful question, not the identity of the person who asked it.

If you need a refresher on how prompt monitoring fits into AI visibility work, Moz’s overview of prompt tracking is a useful starting point. Keep the purpose in mind: measure answers to plausible buyer questions, rather than collect as many prompts as possible.

Step 1: Define the decisions your prompt set must cover

Before searching through transcripts, write down what a buyer needs to decide. This prevents the team from over-collecting easy, top-of-funnel questions and missing the questions that can rule a product out.

Use four intent groups:

1. Category discovery: The buyer is learning what types of solutions exist or building an initial shortlist.
2. Feature and comparison: The buyer is comparing providers, approaches or specific capabilities.
3. Technical validation: The buyer needs to know whether a product fits their systems, workflow, region or requirements.
4. Commercial and purchase: The buyer is checking pricing, plans, implementation, terms or the value of switching.

These groups are a working framework, not labels that an AI platform will automatically assign. A single question can overlap. For example, “Which analytics platform works for a small agency using Shopify and needs client reporting?” asks for category advice, but the constraints make it a comparison question too. Give it one main intent and note the second one if it helps analysis.

Create a simple coverage grid before you collect prompts:

Write down the audience or use case that matters as well. A SaaS team might separate mid-market buyers from enterprise buyers. An e-commerce team might distinguish single-store operators from multi-brand retailers. An agency could track questions by client industry or service category. Do not create segments you cannot explain or act on.

What you should see: A short list of business decisions and a clear definition of the audiences you want the prompts to represent. If your plan is only “track whether we show up in AI”, make the goal more specific. For example: “See whether AI answers include us when operations teams compare inventory tools for a multi-store retailer.”

Step 2: Extract buyer questions from sales calls

Sales calls reveal how prospects describe a problem before they have adopted your product’s terminology. Listen for the point where someone explains their situation, tests an assumption or raises a reason they might not proceed.

Start with calls from relevant deal stages. Include discovery, product evaluation and late-stage discussions if those records are available. Search or filter for themes such as competitor comparison, security, compliance, integration, migration, pricing and team fit. Gong’s documentation explains how its AI Trackers can identify topics in calls; use your own call platform’s equivalent if you have one.

For each relevant call, capture the prospect’s question as closely as you can while removing account-specific details. Prefer the buyer’s actual words over a polished rewrite. If a buyer asks, “Can we use single sign-on without moving everyone to the highest plan?”, do not turn it into “What are the platform’s security features?” The rewrite has lost the plan constraint and the practical purchase concern.

Look for these useful patterns:

• “Can this handle…” followed by a workflow, scale or technical condition.
• “How does this compare with…” followed by a named competitor or current process.
• “What happens if…” followed by a migration, contract or data concern.
• “Do we need…” followed by a plan, integration or implementation question.
• “How much would it cost for…” followed by a team size, usage level or business model.

Separate buyer questions from seller claims. A sales representative may say, “We have enterprise-grade security,” but that is not evidence a buyer asked about a particular security control. If the prospect asks, “Does it support SSO, and is that included in the standard plan?”, that is a usable prompt. The distinction matters because your tracking set should represent demand, not your preferred message.

Turn each good question into a prompt that can stand alone outside the call. Remove “as we discussed” and any pronoun whose meaning depends on the conversation. Keep the audience and constraints where they change the answer. A generic pattern is:

What [product category or capability] is suitable for [audience] that needs [specific condition], and how does [Brand] compare with [Competitor or alternative]?

You do not have to mention your own brand in every prompt. A question such as “What are the best project management tools for a small agency that needs client reporting?” can reveal whether the brand is included without being invited by name. Brand-specific questions are still useful for checking how an engine responds to an explicit comparison or product query.

What you should see: A set of concise prompts with enough context to represent a real deal, but no confidential details. Each entry should include where it came from and the underlying issue, such as “plan eligibility” or “migration risk”.

Step 3: Find conversational queries in Search Console

Search Console gives you real Google query language. It does not show the exact prompts people enter into ChatGPT, and a Google query is not automatically an AI prompt. Use it as one source of buyer phrasing and topic evidence, not as a substitute for AI prompt data.

Open the Search Console performance report and export queries for a relevant period. Filter to the pages, countries or search types that match your market where possible. Keep the query, impressions, clicks and click-through rate, along with the page that appeared. You can use a spreadsheet filter or regular expressions to surface question-shaped and longer queries.

These example expressions help locate candidates:

• Long queries of nine or more words: ^(?:\\S+\\s+){8,}\\S+$
• Question openings: ^(who|what|where|when|why|how|can|is|are|will|should|do|does)\\s.*
• Comparison and commercial wording: (?i)(vs|versus|alternative|alternatives|better than|review|cost of|pricing)

Regular expression support and syntax can vary by tool. Test an expression on a small sample before treating the result as complete. The long-query expression is a practical filter, not a definition of a valuable query. A short query can matter, and a long query can be irrelevant.

Review queries with strong impressions and weaker click-through rates as possible research candidates. They may point to a question people are trying to resolve quickly, but low CTR alone does not prove that users wanted an AI summary. Check the page, search intent and query wording before including it. If the query is an incomplete fragment, retain its useful meaning and turn it into a natural question without inventing extra constraints.

For example, a query such as “inventory management software Shopify multiple warehouses” could become “Which inventory management tools work with Shopify for a retailer managing stock across multiple warehouses?” Only add the retailer context if it is supported by your audience research. If not, keep the prompt broader.

For a step-by-step example of using query data to identify buyer language, see Vizzybl’s guide to extracting buyer prompts from Search Console. Treat any filtering advice as a way to find candidates. Your own review still decides what belongs in the tracked set.

What you should see: A list of relevant search queries with their landing pages and performance context. Each selected item should have a clear relationship to a decision in your coverage grid. Remove navigational searches, unrelated informational phrases and queries that do not match the market you intend to measure.

Step 4: Mine support and pre-sales conversations

Support conversations often expose product fit questions that never appear in public search data. Pre-sales chat can be especially useful because the prospect is testing a specific requirement before committing. Existing customers can also reveal recurring setup and capability questions that future buyers may ask.

Search or tag tickets for themes such as integrations, plan limits, permissions, checkout behaviour, data migration, compliance, implementation and troubleshooting. Where possible, separate pre-purchase questions from post-purchase support. A question from an existing customer may still matter for product education, but it does not always represent a buying decision.

Take a ticket question such as, “Will this connect to our BigCommerce store and update inventory without a manual import?” Remove the customer’s name and account details. Then ask whether the question makes sense without more context. If it does, preserve it. If the answer depends on a detail, retain only the detail that changes the answer, such as the platform or type of workflow.

Avoid turning every support issue into a tracked prompt. A one-off bug report, internal process question or question about a retired feature may not deserve a place in the baseline. Look for repeated uncertainty, a high-value purchase blocker or an important technical condition. You can record a lower-frequency question if the consequence of getting it wrong is significant, but explain that reason in the priority field.

What you should see: Prompts that test whether an AI answer can explain practical fit, not just describe features. Include the conditions that tend to disqualify a product, such as a required integration or an essential plan capability.

Step 5: Clean, combine and score the candidates

At this point, you may have several versions of the same buyer need. Do not track all of them just because they came from different sources. A large pile of near-duplicates can make reporting harder to interpret and can give one topic too much weight.

First, standardise each prompt without flattening its meaning. Fix transcription errors, remove identifying details and expand unclear shorthand. Keep wording natural. Do not rewrite a buyer’s blunt question into promotional language.

Next, combine duplicates when the answer you want to measure is essentially the same. Keep them separate when a changed detail could reasonably change the recommendation. For instance, “best tool for a small team” and “best tool for a regulated enterprise with SSO requirements” are different evaluation needs, even if both concern the same category.

Use a simple priority score to make decisions. Score each candidate from one to three against these criteria:

• Evidence: Is it supported by one or more customer or search sources?
• Buyer importance: Could the answer affect a shortlist, a purchase or a disqualification?
• Strategic fit: Does it relate to an audience, product or market you actively serve?
• Actionability: If the answer is weak or inaccurate, can your team improve the content or product information that supports it?

Add the scores, but do not pretend the total is a scientific measure. It is a sorting aid. A high score means “review this early”, not “this prompt is guaranteed to produce meaningful AI visibility data”. Record why an exception stays in the set. For example, an important compliance prompt may be rare but too consequential to ignore.

Then decide how many prompts your team can review properly. A first set of around 40 carefully chosen prompts is a workable exercise for a team building its process; it is a practical starting recommendation, not an industry benchmark. If you have limited tracking capacity, prioritise coverage across the four intent groups and your most important audiences before adding more variants. Search Engine Land’s reporting on real ChatGPT search prompts also illustrates why observed user language can differ from marketer assumptions.

What you should see: A deduplicated list where every prompt has a source, an intent, an audience or use case, and a reason for inclusion. If you cannot explain why a prompt is there, remove it or return it to the candidate list.

Step 6: Preserve meaningful wording variation

Two questions can express the same need and still produce different answers. That does not mean you should create dozens of minor rewrites. It means you should recognise when a wording change represents a different user intent or a meaningful change in framing.

Separate variants when they change one of these things:

• Recommendation request: “What options should I consider?” versus “Which option should I choose?”
• Comparison target: Named competitor versus a category-level shortlist.
• Positive or negative framing: Asking for benefits versus asking what could go wrong.
• Buyer context: Small team, large organisation, agency or specialist use case.
• Decision stage: Learning about a category versus checking a plan before purchase.

Keep a small number of variants where you have evidence they occur in your sources or where the wording tests a distinct decision. Label them as a group so you can compare like with like. Do not treat one wording version as the “correct” prompt and discard the language buyers actually use.

In its prompt-set research, Momentic discusses how prompt framing can affect brand mentions and citations. Use its guide to building a prompt set worth tracking as a reminder to separate informational prompts from recommendation or negative-framing prompts in your analysis. The practical takeaway is to record the intent and framing so that a change in answer is interpretable.

What you should see: A core prompt for each buyer need, with a small, labelled set of meaningful variants where necessary. Your spreadsheet should make it obvious whether a prompt is a new topic or simply a different wording of an existing one.

Step 7: Turn the set into a tracking baseline

Before measuring, check that each prompt is understandable on its own and that the wording reflects a plausible buyer question. Remove internal shorthand, unexplained acronyms and instructions that would not normally come from a customer. Keep a separate note for the intended audience and question source; do not insert hidden context into the prompt itself.

Choose the AI engines that matter to your audience and tracking purpose. Use the same prompt wording when comparing engines. If your tool supports daily or scheduled tracking, make sure the schedule and engine list are documented. If you check manually, record the date, engine and any relevant settings so another person can repeat the check.

For each answer, capture more than a yes or no. Record:

• Whether your brand is named, cited or linked.
• Whether competitors or alternatives are named.
• The context in which your brand appears, if it appears.
• Any claim about your product that is useful, missing or inaccurate.
• The cited pages or sources shown by the engine, where available.
• The date, engine and exact prompt used.

Define “brand visibility” before you begin. A passing mention is not the same as a citation, a recommendation or a link to a useful page. Your reporting can include separate columns for each, rather than combining them into one vague visibility score. Also decide how you will handle answers that change between checks. AI answers can vary, so a single result is an observation, not proof of a lasting pattern.

A platform can make repeated monitoring and review more manageable. SearchFIT provides AI visibility tracking alongside AEO, scheduled content and analytics; see SearchFIT pricing to assess whether a platform fits your team. If you are considering an internal workflow, browse the SearchFIT resources for related material. Whatever tool you choose, keep your prompt list and definitions documented so the results remain interpretable.

What you should see: A baseline record that another teammate could reproduce. The prompt, engine, date, visibility definition and result fields should all be clear. If two people would score the same answer differently, write down a rule before comparing the data.

Step 8: Check whether the set reflects real buyer coverage

Run a coverage review before presenting results. Start with the intent grid: do you have prompts for discovery, comparison, technical validation and purchase? Check the audience and use case columns too. A list can contain many prompts and still overrepresent one buyer type or one easy-to-answer topic.

Use these checks:

• Source balance: Are sales, Search Console and support represented where those sources exist? If one source dominates, is that because it reflects your business or because it was easiest to export?
• Decision balance: Do you cover early research and late-stage questions, including reasons a buyer might reject the product?
• Specificity: Do prompts include important constraints without becoming overloaded with several unrelated questions?
• Independence: Can each prompt be understood without access to a call, ticket or internal document?
• Evidence: Can you trace each prompt back to a source or a clearly documented strategic reason?
• Action: If an answer is poor, can the team identify a page, product fact or content gap to investigate?

Ask a colleague who did not build the list to review a sample. Give them the source phrase and the tracked prompt, then ask whether the prompt preserves the buyer’s intent. This is a simple way to catch marketing edits that have made a question sound more like a headline than a real request.

You can also compare your prompt set with your existing content. If the prompts ask about implementation, migration and plan limits but your site only has broad feature pages, you have found a useful content gap. If the answers cite third-party review pages for product details, check whether your own pages provide clear, current and specific information. Do not assume that publishing a page will guarantee a citation; use the gap as a hypothesis to test.

What you should see: A list with balanced coverage and a clear route from weak AI answers to a business action. If results are difficult to interpret, reduce duplicates, clarify the intent labels or split prompts that combine multiple buyer questions.

Step 9: Maintain the prompt set as buyer needs change

A prompt set should not be frozen after the first report. Products change, competitors change and customer concerns shift. Revisit the source material on a regular schedule and update the set when there is evidence of a new or obsolete question.

A practical maintenance routine is:

• Review new sales call themes and support tags monthly.
• Check Search Console query exports when you do your normal reporting review.
• Add a prompt when it represents a meaningful new buyer need, not just because it is a newly discovered phrase.
• Retire prompts about discontinued products, old plans or markets you no longer serve.
• Keep a change log with the date, reason and source for each addition or removal.
• Preserve a stable core for comparisons, while labelling newer prompts as exploratory until you have enough observations to interpret them.

Do not silently replace one prompt with a different one and compare the scores as if nothing changed. Keep the old wording in the change log. If a question needs a substantive rewrite, treat that as a new prompt or version so you can distinguish a change in user intent from a change in the AI answer.

A monthly review is a sensible operating rhythm for many teams, but use a faster cycle if your product or market changes quickly. The important thing is that the set changes because evidence changed, not because someone wants a larger dashboard.

Common mistakes to avoid

Writing prompts from product messaging. Marketing copy describes how you want the product understood. Buyer language reveals what someone is trying to solve. Start with the source question and only edit it enough to make it standalone.

Tracking only prompts that name your brand. These are useful for checking direct product questions, but they cannot show whether an engine includes you in an open category recommendation. Keep brand-led and brand-neutral prompts distinct.

Treating Search Console as AI prompt data. Search Console reports Google search queries. It can help you find conversational language and related needs, but it does not reveal the exact prompts used in separate AI assistants.

Adding every possible wording variation. Near-duplicates inflate the list and make results harder to review. Group variants only when they test a distinct framing, audience or decision.

Scoring mentions as recommendations. A brand named in a caveat is not equivalent to a strong recommendation or a useful citation. Separate mention, sentiment, citation and recommendation in your reporting definitions.

Ignoring technical and commercial blockers. Category prompts may show whether the brand is recognised, while integration, plan and migration questions show whether it is a plausible choice. Include both kinds of decision.

How to know the process worked

Your first prompt set is ready when another marketer can explain what each prompt tests, where it came from and what the team will do with the result. It should represent the real decisions your audience makes, include the strongest available evidence from customer conversations and Google search, and remain small enough for consistent review.

The set is not successful simply because an AI engine mentions your brand. It is useful when the results help you spot a gap, validate a change or understand how different buyer questions shape the answers. Track the same well-defined prompts over time, keep the wording and scoring rules visible, and update the list only when your evidence or business priorities justify it.

For your next step, choose one audience, collect a sample from sales calls, Search Console and support, and build a first set you can review together. Then use SearchFIT pricing to see whether scheduled AI visibility monitoring is a fit for how your team plans to run the process.