Choosing which prompts to monitor is one of the most important parts of AI visibility testing.

It is also easy to overcomplicate.

You could ask an AI tool to generate 100 questions about your business. You could collect phrases from search tools. You could create dozens of slight variations of the same query.

But none of that guarantees you are testing the questions that actually matter to potential customers.

A more useful starting point is the customer journey.

Instead of asking:

What prompts should we monitor?

start with:

What problems, questions and decisions do real customers encounter on their way towards buying from a business like ours?

Then turn a representative selection of those situations into prompts.

This guide provides a practical way to do that.

Step 1: Decide Which Customers You Want to Understand

Start with the people the business genuinely wants to attract.

An accountant might serve manufacturers, property investors and construction companies.

A gym might particularly want beginners, experienced lifters or people attending group classes.

Different customers have different questions.

You therefore don’t need to begin with every service the business offers. Pick an important customer group and ask:

• What problem brings them to us?
• What are they trying to achieve?
• What might stop them choosing us?
• What information affects their decision?
• What makes us especially suitable — or unsuitable — for them?

This provides the foundation for the prompt set.

Step 2: Talk to People, Not Just Search Tools

Some of the best prompt ideas may already exist inside the business.

Speak to:

• customers;
• sales staff;
• reception or customer-service teams;
• account managers;
• support staff;
• anyone regularly involved in enquiries.

Ask questions such as:

What do prospective customers normally ask first?

What do they worry about?

What questions keep coming up?

What information do they want before requesting a quote or making a purchase?

What do people say just before you realise your business can help them?

Customer emails, enquiry forms and sales-call notes can be equally useful.

We have previously discussed using real customer situations when building an AI visibility query set. The important principle is that prompt research need not be entirely digital. Your business may already know a great deal about the questions customers actually ask.

Step 3: Map the Questions Along the Customer Journey

You don’t need a complicated marketing funnel.

A simple journey might be:

Problem → Exploration → Requirements → Comparison → Suitability → Decision

Think about what the customer is trying to establish at each stage.

Stage	What might the customer be asking?
Problem	What is my problem and do I need help?
Exploration	What kinds of solutions exist?
Requirements	What features, services or expertise do I need?
Comparison	Which businesses or products could help?
Suitability	Which option fits my particular circumstances?
Decision	Which one should I contact, visit or buy from?

Not every customer follows this exact sequence.

The purpose is simply to make sure your prompt list covers different parts of a realistic buying journey rather than clustering around one broad search.

Step 4: Add Digital Evidence

Now supplement what the business knows with information from places such as:

• Google Search Console;
• reviews;
• forums and communities;
• competitor websites;
• internal website searches;
• support tickets;
• related searches;
• AI conversations.

Digital sources can reveal questions you hadn’t considered.

But they should supplement customer understanding rather than replace it.

The objective isn’t to find the largest possible collection of phrases.

It is to understand the needs those phrases represent.

Step 5: Group Questions by Need, Not Exact Wording

Suppose customers ask:

Do you have fixed fees?

Can I pay the same amount each month?

Will I know what my accountancy bill is going to be?

These are different sentences.

But they may represent essentially the same customer need:

predictable pricing.

That distinction matters enormously for AI visibility testing.

One hundred slight wording variations do not necessarily represent 100 different customer situations. Your query set should aim for coverage of meaningful needs, not volume for its own sake.

Step 6: Turn the Important Needs Into Representative Prompts

Now create prompts covering the journey.

They do not need to represent every possible way a customer could express the need.

They simply need to be plausible and commercially useful.

A small business might initially choose:

• two or three early-stage prompts;
• several requirement or problem-specific prompts;
• several comparison prompts;
• several buyer-ready prompts.

That might produce 10–20 useful starting prompts rather than 100 largely overlapping ones.

The exact number matters less than what the prompts cover.

Step 7: Apply Two Final Checks

Before adding a prompt to the core list, ask two questions.

The commercial check

If our business appeared perfectly in this answer, could the person asking realistically become a customer?

A broad informational query may still matter, but it may deserve less attention than one closely connected with a real customer problem.

This connects directly with the RAMP Framework: visibility becomes more useful when the underlying query itself matters commercially.

The realism check

Could I realistically imagine a customer asking this — either in one message or across a conversation?

If not, the prompt may have become over-engineered for the test.

Step 8: Pilot the List Before Treating It as Permanent

Your first prompt list is a starting hypothesis.

Run the prompts.

Some may produce poor or irrelevant responses.

Some may overlap.

Others may reveal requirements or competitors you had not considered.

That is useful information.

Remove weak prompts. Add better ones. Refine the set as you learn.

Prompt selection should be an evolving process rather than a one-time exercise.

Worked Example: Building an AI Visibility Prompt Set for a Gym

Consider a hypothetical independent Birmingham gym called Riverside Fitness.

It offers general membership, gym inductions, personal training and flexible monthly membership.

One of its most important customer groups is adults returning to exercise after a long break who may feel nervous about joining a gym.

Instead of starting with:

What gym keywords should we track?

Riverside starts with the customer.

What Does the Gym Hear From Prospective Members?

Reception staff and trainers might report questions such as:

I haven’t been to a gym for years. Will I feel out of place?

Will somebody show me how the equipment works?

I’m very unfit. Is the gym suitable for beginners?

Do I need to sign a 12-month contract?

Can I get some help without having a personal trainer every session?

When is the gym busiest?

These questions immediately tell us more than a generic phrase such as:

gym Birmingham

Map Those Needs Across the Journey

Journey stage	Customer need	Possible prompt
Problem	Wants to start exercising again	How should I start exercising again after several years?
Exploration	Considering joining a gym	Is joining a gym a good option if I’m very unfit?
Requirements	Needs beginner support	What should I look for in a gym if I haven’t exercised for years?
Comparison	Comparing providers	Which Birmingham gyms are good for nervous beginners?
Suitability	Wants specific support	Which Birmingham gyms offer proper inductions and beginner support?
Decision	Ready to choose	Which Birmingham gym offers beginner support, flexible membership and personal training without a long contract?

That is already a useful skeleton for the prompt set.

Add Different Customer Circumstances

Riverside could then test related situations such as:

• someone returning to exercise after ten years;
• someone worried about using gym equipment;
• someone wanting evening access after work;
• someone wanting occasional personal training;
• someone specifically avoiding long contracts.

These aren’t merely synonyms.

They can materially change what makes a gym suitable.

Build the First Monitoring Set

The first prompt portfolio might contain something like:

Early journey

• How should I start exercising again after a long break?
• What should a beginner look for when choosing a gym?
• Is a gym suitable for someone who is currently very unfit?

Exploration and requirements

• What type of gym is best for someone nervous about exercising?
• What support should a good gym provide new members?
• Should a beginner look for a gym that provides an induction?

Comparison

• Which Birmingham gyms are particularly suitable for beginners?
• Which Birmingham gyms offer inductions for new members?
• Which Birmingham gyms offer both normal membership and personal training?

Closer to a decision

• Which Birmingham gym should I consider if I haven’t exercised for years and want help learning the equipment?
• Which Birmingham gyms offer flexible monthly membership without a long contract?
• Which Birmingham gym would suit a nervous beginner wanting an induction, personal-training support and evening access?

That is not every gym-related prompt someone could ask.

It doesn’t need to be.

It provides representative coverage of an important customer journey.

But Do Real People Really Write Such Detailed Prompts?

This is a reasonable question.

Some of the prompts used in AI visibility testing can become considerably longer than traditional search queries.

Not everyone will type all their requirements into the first message.

However, AI search is encouraging longer, more specific questions and follow-up conversations. Google itself says users of its AI search experiences ask longer and more specific questions and use follow-ups to explore further. (Google for Developers)

So the same gym customer might begin with:

Which Birmingham gyms are good for beginners?

Then ask:

Which of those offer proper inductions?

Then:

I don’t want a long contract. Which would suit me now?

By the end of the conversation, the AI has effectively received the same information that could have been contained in one longer prompt.

For testing purposes, we may sometimes combine those requirements into a controlled scenario:

I’m returning to exercise after several years and want a Birmingham gym that is welcoming to beginners, provides an induction and offers flexible monthly membership. Which gyms should I consider?

That doesn’t mean we believe every customer writes exactly that sentence.

It means we are testing how AI responds when those realistic customer requirements are present.

A useful test prompt therefore needs to be plausible, not necessarily a verbatim imitation of an average search.

Your Monitoring Prompt Is Not a Keyword to Put on the Webpage

This is another important distinction.

Suppose Riverside tests:

I’m 48, haven’t exercised for years and feel nervous about joining a gym. I want somewhere in Birmingham with an induction, friendly support and flexible monthly membership. Which gyms should I consider?

The lesson is not:

Put that sentence in the page title, H1 and several paragraphs.

The prompt represents an information need.

The website should instead make the relevant information genuinely easy to find:

• Is the gym suitable for beginners?
• What happens during an induction?
• Will someone explain the equipment?
• Is personal training available?
• How flexible are the memberships?
• What does membership cost?
• What are the opening hours?
• When is the gym busiest?
• What should someone expect on their first visit?

Google’s current guidance for generative AI search specifically says site owners do not need to capture every long-tail variation or use the exact words contained in a query. Its systems can understand synonyms and general meaning, while useful, well-organised, people-first content remains the priority. (Google for Developers)

That doesn’t make headings or normal SEO practices unimportant. Google continues to recommend clear structure and useful headings. The difference is that those headings should help the reader understand the page rather than simply provide somewhere to repeat an exact search phrase. (Google for Developers)

The better principle is:

Build resources that answer the customer’s need, not pages designed merely to repeat the customer’s wording.

Prompt Research Can Also Become a Website Audit

Once Riverside has mapped the journey, another useful question becomes obvious:

Does our website actually answer these customer questions?

Perhaps the gym really is excellent for nervous beginners but says almost nothing about inductions.

Perhaps flexible memberships exist but the terms are difficult to find.

Perhaps personal training is available but there is no explanation of how a beginner would access it.

Those are genuine communication gaps.

Improving them does not guarantee that Riverside will suddenly appear in an AI recommendation.

Competition, competing information, the AI system being used and normal response variability can all affect visibility.

But making useful information clearer is still a sensible improvement for prospective customers.

That is why AI visibility testing is best treated as a process rather than a formula for guaranteed rankings.

A Simple Customer-Journey Prompt Worksheet

Any business can start with this:

Journey stage	What is the customer trying to do?	What do customers actually ask?	What circumstances matter?	Representative prompt	Commercial priority
Problem					High / Medium / Low
Exploration
Requirements
Comparison
Suitability
Decision

Fill it in using what customers, sales teams and other customer-facing people actually know.

Add digital evidence.

Group similar questions into underlying needs.

Then create a manageable set of representative prompts and start testing.

The Goal Is Not to Predict Every Search

There will never be a complete list of everything a potential customer could ask an AI system.

That shouldn’t be the objective.

The objective is to understand:

Who are our customers?

What problems bring them to us?

What information do they need as they move towards a decision?

Which circumstances materially affect which business is suitable?

And are we visible when those commercially meaningful needs are expressed?

Start with the customer journey.

Build representative prompts around the needs within it.

Test them.

Learn from the results.

And refine the prompt set as your understanding of the customer and the market improves.

That is a much more useful starting point than simply trying to build the longest possible list of AI search queries.