How to Choose Prompts to Track for AI Visibility

Choosing what prompts to track is one of the most important decisions in AI visibility measurement. Track the wrong questions and you can collect plenty of data without learning much about how buyers encounter your brand.

The best prompt sets usually start with evidence you already have: Google Search Console queries, keyword research, sales conversations, support tickets and the questions customers repeatedly ask. From there, translate those signals into the more conversational language people use with ChatGPT, Gemini, Perplexity and other AI engines.

You don't need to predict every possible prompt. You need a representative set of questions covering the topics and buying situations that matter to your business.

Prompts and keywords are related, but they aren't the same thing

SEO teams have spent years choosing keywords using search volume, intent, rankings and competition. AI prompt tracking has fewer established demand signals.

There is no equivalent of Google Search Console that tells you exactly how many times people asked ChatGPT:

What's the best CRM for a small consultancy with five salespeople?

Some AI visibility platforms now estimate prompt or topic demand, but there is no universal first-party dataset comparable with Google search volume.

That doesn't make traditional keyword data obsolete.

Search behavior gives you evidence that people care about a topic. If thousands of people search for a product category, comparison or problem each month, it is reasonable to investigate how that same need might be expressed in an AI conversation.

The mistake is uploading your keyword list unchanged.

A keyword such as:

crm small business

might become:

What is a good CRM for a small business?

And then a more specific variation:

What is a good CRM for a 10-person B2B sales team?

Your keyword research tells you which subjects have demand. Your prompt research tells you how people might discuss those subjects with an AI assistant.

1. Start with Google Search Console

Google Search Console is one of the strongest places to find initial prompts because the queries come from people who already encountered your website in search.

Start by looking for longer queries and questions containing terms such as:

• >how
• >what
• >why
• >which
• >should
• >can
• >best
• >vs
• >alternative
• >for

You don't need to track the query exactly as it appears.

A Search Console query such as:

best payroll software small business

could become:

What's the best payroll software for a small business?

If you serve different customer segments, you might also add:

What's the best payroll software for a small business with 20 employees?

The impression count in Search Console is useful here. It isn't AI prompt volume, but it tells you that the underlying question or topic already has observable search demand.

Look beyond queries where you rank well. High-impression topics where competitors outperform you can be particularly useful to monitor in AI search.

2. Use keyword research as a demand signal

Keyword tools such as Ahrefs can expand the picture beyond the queries your own website already receives impressions for.

Look at:

• >your core commercial keywords
• >high-volume informational topics
• >comparison searches
• >"[competitor] alternatives"
• >"best [category]"
• >"[product] for [audience]"
• >problems your product solves

Search volume shouldn't determine your entire prompt set. It should help you avoid spending too much tracking capacity on questions with little evidence of demand.

For example, suppose you're choosing between these topics:

CRM software for small businesses – substantial established search demand

CRM for architecture firms using a four-day working week – almost no measurable search demand

The second question could still matter if architecture firms are an important customer segment. But absent any customer or sales evidence, the first deserves a tracking slot sooner.

This gives you a useful hierarchy:

Observed customer demand > observed search demand > plausible ideas generated in a brainstorm.

Keyword data is especially useful for the middle layer.

3. Look at People Also Ask and related searches

Google's People Also Ask results are useful because they reveal adjacent questions around a topic.

Search for one of your core product categories or customer problems, then inspect the questions Google surfaces around it.

Suppose you sell accounting software and search for:

accounting software small business

You might find questions around:

• >whether small businesses need accounting software
• >which software is easiest to use
• >alternatives to a well-known provider
• >software suitable for freelancers
• >software that handles VAT

These are separate prompt opportunities because they represent different user needs.

Autocomplete and related searches can provide another layer of variations.

Don't turn every variation into a tracked prompt. Use them to understand the topic first, then select representative questions.

4. Mine sales calls, support tickets and customer questions

Your customers are often a better prompt generator than your marketing team.

Sales calls reveal the questions people ask before choosing a product:

• >Does this work for a company our size?
• >How does it compare with X?
• >Is there a cheaper alternative?
• >Which option works best for our use case?
• >Can it integrate with the tools we already use?

Support tickets reveal another category: the problems customers are trying to solve.

These questions matter because buyers don't always start an AI conversation by naming the product category.

Someone might not ask:

What is the best project management software?

They might ask:

How can I keep track of client projects when work is split between Slack, email and spreadsheets?

The second prompt reaches the same market from the problem side.

This is also where you find language keyword tools often miss: company size, industry constraints, budget concerns and the vocabulary customers actually use.

5. Cover different stages of the decision

A prompt set made entirely of "best X" queries gives you a narrow view of AI visibility.

Include prompts from several types of decision.

Problem discovery

The user knows the problem but may not know the category yet.

How can I monitor whether my company appears in ChatGPT recommendations?

Why is my organic traffic declining while search impressions are increasing?

These prompts can reveal which companies AI introduces when explaining a problem.

Category discovery

The user knows what type of solution they need.

What are the best AI visibility tools?

Which software can track how my brand appears in ChatGPT?

This shows whether your brand enters the initial consideration set.

Comparison and evaluation

The user is actively narrowing the options.

What are good alternatives to [Competitor]?

Which AI visibility platform works best for an SEO agency?

This is usually commercially important because the answer can directly shape a shortlist.

Brand-specific questions

The user already knows you.

What does Vercite do?

Is Vercite suitable for an SEO agency?

These are useful for monitoring how your brand is described, including factual accuracy and sentiment, but they should be analyzed separately from unbranded visibility.

If the brand name is already in the prompt, appearing in the answer does not tell you much about whether the AI would have selected the brand independently.

6. Add persona variations where they change the answer

Generic prompts are useful as a baseline. Real users often provide more context.

Compare:

What's the best CRM?

with:

What's the best CRM for a five-person B2B sales team?

or:

What's the best CRM for an ecommerce company that mainly sells through Shopify?

Use these variations selectively.

Creating separate prompts for every combination of industry, country, company size, price range and feature can turn a useful tracking set into hundreds of almost identical questions.

Add a variation when the additional context could plausibly change which brands should be recommended.

7. Use AI to expand the list, not create the strategy

ChatGPT, Gemini or another LLM can help generate prompt ideas once you have a strong foundation.

For example, give it:

• >your product categories
• >customer types
• >common objections
• >Search Console questions
• >keyword clusters
• >sales questions

Then ask it to suggest realistic ways someone might express those needs conversationally.

Vercite can also generate suggested prompts when you set up a brand. This can give you a useful starting point, especially when you are building a prompt set from scratch. The same rule still applies: review the suggestions against your actual customers, search demand and commercial priorities before deciding what to track.

Treat generated prompts as candidates.

An LLM can generate a convincing question without any evidence that customers care about it. AI-generated prompts become far more useful when you can connect them back to search demand, customer research, sales data or an important business segment.

How to decide which prompts make the final list

By now you may have 100 or more potential questions.

Don't track all of them.

For each prompt, ask four things.

Is there evidence people care about this?

Evidence can come from:

• >Search Console impressions
• >keyword search volume
• >People Also Ask
• >sales conversations
• >support questions
• >customer research
• >forum discussions
• >existing conversion data

Several sources pointing toward the same topic make the case stronger.

Does the prompt matter commercially?

Consider what happens if a competitor consistently appears and you don't.

A broad educational question might still be useful, but prompts close to product discovery, comparison and evaluation often deserve higher priority.

Does it add new information?

Ten slightly different versions of:

What are the best CRM tools?

will mostly measure the same topic.

A stronger set could cover:

• >best CRM
• >CRM for small companies
• >CRM for ecommerce
• >HubSpot alternatives
• >CRM with marketing automation
• >how to manage sales leads
• >CRM comparison for a specific use case

The goal is coverage across meaningful decisions, not maximum prompt count.

Could the answer change your actions?

This is a useful final filter.

If poor visibility for a prompt could lead you to create content, improve positioning, pursue mentions on influential third-party sites or investigate competitor visibility, it is worth measuring.

If you'd do nothing differently regardless of the result, its value as a tracked prompt is questionable.

Organize prompts by topic, not as one giant list

Individual AI answers vary.

This makes groups of related prompts more useful than obsessing over one exact wording.

In Vercite, prompts can be tagged so related questions can be analyzed together.

For example:

Accounting software

• >General category
• >Small business
• >Ecommerce
• >Freelancers
• >Alternatives
• >Integrations

Or for a hotel company:

Family travel

• >Family hotels
• >Hotels with pools
• >Weekend breaks
• >Hotels near attractions
• >Premium family hotels

You can then inspect AI visibility for the broader topic while still being able to open the individual responses behind it.

This helps separate a genuine visibility pattern from one unusual answer.

Keep a stable core prompt set

Prompt tracking becomes more valuable over time.

If you replace half your prompts every time you find a new idea, comparisons between months become difficult. A change in overall visibility could come from the AI engines, your actual visibility or merely the prompts you changed.

Keep a stable core set covering your most important topics.

Then reserve part of the set for experimentation.

You might test:

• >a new customer segment
• >a new product category
• >an emerging competitor
• >a growing search topic
• >a different persona
• >a new market

If the experimental prompts continue producing useful information, move them into the core set during a planned review.

The principle is simple: refine periodically, but don't continuously rebuild the prompt set.

Track the same prompts across AI engines

Don't create one prompt list for ChatGPT and an unrelated one for Gemini.

Where possible, run the same core prompt set across the engines you care about.

That makes questions such as these answerable:

• >Are we visible in ChatGPT but absent from Gemini?
• >Does Google AI Mode recommend different competitors?
• >Which engines cite our own website?
• >Does our positioning change depending on the engine?

Vercite tracks the full responses returned for the prompts you monitor, so you can compare brand visibility, competitors, citations and how the brand is framed between engines and over time.

The prompt becomes the controlled input. The AI answer is what you measure.

A practical prompt-selection process

You don't need a complicated framework to get started.

1. >Export relevant question and long-tail queries from Google Search Console.
2. >Pull important keyword clusters and their search demand from your keyword tool.
3. >Collect repeated questions from sales, support and customers.
4. >Review People Also Ask, autocomplete and relevant community discussions.
5. >Turn the strongest topics into natural conversational questions.
6. >Add a limited number of persona and use-case variations.
7. >Separate branded prompts from category and discovery prompts.
8. >Group prompts using meaningful topic tags.
9. >Keep the strongest prompts stable so you can measure changes over time.
10. >Add and remove experimental prompts during planned reviews rather than continuously changing the set.

You will probably discover far more prompts than you need.

That's useful. Keep a backlog.

The job isn't to monitor every way someone could phrase a question. It's to build a representative set that tells you whether your brand appears when the right audiences ask the questions that matter.