The Prompt-Set Template for AI Visibility Monitoring: 60 Buyer-Journey Queries You Can Adapt in 20 Minutes

Last updated July 2026

Every AI visibility dashboard is only as good as the prompts feeding it. A share-of-voice number calculated from ten generic queries tells you almost nothing. A share-of-voice number from 60 prompts that map to how your buyers actually research your category tells you exactly where you are visible, where competitors outrank you, and which funnel stages need the most work.

This template gives you the structure to build that corpus in about 20 minutes. Swap the bracketed slots for your real category, product names, and competitors, then load the set into the monitoring tool of your choice.

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How to use this template

Each section below covers one funnel stage. Every stage has 15 prompts with bracketed placeholders in square brackets. Replace them as follows:

• [category]: your product category (for example, “project management software,” “cybersecurity platform,” or “email marketing tool”)
• [your brand]: your exact brand name as it appears in public coverage
• [competitor A], [competitor B]: two to three of your primary rivals
• [use case]: a specific job-to-be-done your product addresses (for example, “automate client reporting” or “reduce churn”)
• [persona]: a buyer segment you want to track separately (for example, “small agency,” “enterprise IT team,” or “solo founder”)

Run each prompt across at least four AI engines: ChatGPT, Perplexity, Google AI Overviews, and Gemini. According to Profound’s analysis of 100,000 prompts, only 11% of cited domains overlap between ChatGPT and Perplexity, so treating any single engine as representative gives you a badly distorted picture of your actual visibility (Profound, July 2025; note: single-vendor research).

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Stage 1: Awareness and category queries

These prompts fire at the top of the funnel. The buyer does not know your brand yet. They are defining the problem and discovering that a category of solutions exists.

Weight: 1x (see weighting rules below)

1. What is [category]?
2. How does [category] work?
3. Why do companies use [category]?
4. What are the benefits of [category]?
5. How is [category] different from traditional [adjacent category]?
6. What problems does [category] solve?
7. Is [category] worth it for a [persona]?
8. When do you need [category]?
9. What should I know before buying [category]?
10. How big is the [category] market?
11. What are the main use cases for [category]?
12. Who uses [category]?
13. How has [category] changed in the last year?
14. What does a [category] platform actually do?
15. Can a [persona] run [category] without a specialist?

What to track: brand mentions are rare at this stage. Track whether your brand appears at all and note which competitors get named as category anchors. Those competitors own the definitional narrative, which is hard to displace later.

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Stage 2: Consideration and comparison queries

The buyer now knows the category exists and is evaluating options. These prompts map to the period when buyers build shortlists and run comparisons.

Weight: 2x

16. Best [category] tools in 2026
17. Top [category] software for [persona]
18. [Category] tools compared
19. [Competitor A] vs [competitor B]
20. [Competitor A] vs [competitor B] vs [competitor C]
21. What is the best [category] platform for [use case]?
22. [Category] tools with the best [specific feature]
23. Affordable [category] platforms for [persona]
24. What [category] tool do agencies use?
25. [Category] software with the best integrations
26. How do I choose a [category] tool?
27. What features matter most in a [category] platform?
28. [Category] tools that include [specific feature]
29. Which [category] platforms are easiest to set up?
30. Free vs paid [category] software: what is the difference?

What to track: share of voice is the primary metric here. Count how many of these 15 prompts name your brand, then divide by 15 for your consideration-stage mention rate. Track competitors on the same basis.

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Stage 3: Decision and brand-direct queries

The buyer has a shortlist. These prompts name your brand directly. They are researching you, not the category.

Weight: 3x

31. What is [your brand]?
32. How does [your brand] work?
33. Is [your brand] legit?
34. [Your brand] reviews
35. [Your brand] pricing
36. [Your brand] vs [competitor A]
37. [Your brand] vs [competitor B]
38. Is [your brand] worth it?
39. What do users say about [your brand]?
40. [Your brand] pros and cons
41. Who is [your brand] best for?
42. Does [your brand] integrate with [common tool]?
43. [Your brand] free trial: what do you get?
44. Is [your brand] better than [competitor A] for [persona]?
45. [Your brand] alternatives

What to track: accuracy is as important as presence here. Do AI engines describe your pricing, features, and positioning correctly? Inaccurate descriptions at the decision stage are the most damaging because the buyer is closest to making a choice. Log both whether your brand is mentioned and whether the description is accurate, positive, neutral, or negative.

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Stage 4: Problem-solution queries

These prompts do not name a category or a brand. The buyer describes a pain point and expects AI to recommend an approach or a tool. This stage is undertracked by most teams and often reveals unexpected competitors.

Weight: 2x

46. How do I [use case] without [pain point]?
47. What is the fastest way to [use case]?
48. How do I know if [problem] is costing me [outcome]?
49. How do [persona] usually handle [problem]?
50. I need to [use case] but my team is too small for enterprise software
51. What tools help with [specific pain point]?
52. How do I [use case] at scale?
53. Is there a way to automate [use case]?
54. How do I measure [use case] success?
55. What should I do if [problem] is getting worse?
56. How do I get started with [use case] quickly?
57. What is a realistic timeline for [use case] results?
58. Can I [use case] without a big budget?
59. What do experts recommend for [problem]?
60. How do I explain [problem] to leadership and get buy-in?

What to track: note which brands AI engines recommend in response to these prompts. You are likely to see specialist competitors or adjacent-category tools that never appear in your explicit brand comparisons. These are the competitors eating your funnel from the top.

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Weighting rules

Not all 60 prompts are equal. A buyer asking “Is [your brand] worth it?” (Stage 3) is closer to purchasing than a buyer asking “What is [category]?” (Stage 1). Weight your share-of-voice calculation to reflect that.

Stage	Weight	Rationale
Stage 1: Awareness	1x	Definitional queries; brand mention is informational, not commercial
Stage 2: Consideration	2x	Shortlist-building; brand mention signals evaluation interest
Stage 3: Decision	3x	Brand-direct queries; appearance here is the highest commercial signal
Stage 4: Problem-solution	2x	High intent but no explicit brand; appearance here signals strong retrieval authority

Weighted share of voice formula:

Calculate your score per stage (mentions divided by prompts in that stage), multiply by the stage weight, sum across all four stages, then divide by the total weight (1+2+3+2 = 8) to get a single weighted share-of-voice number for your brand.

This number is more meaningful than a flat average because it penalizes brands that only appear in definitional queries and rewards brands that show up when buyers are ready to decide.

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Tagging schema

Tag every prompt before you load it into your monitoring tool. Consistent tags let you filter, segment, and compare slices of your prompt set without rebuilding the analysis each time.

Tag dimension	Values	Purpose
stage	awareness, consideration, decision, problem-solution	Enables per-stage share-of-voice calculations
persona	[your buyer segments, e.g. “smb,” “agency,” “enterprise”]	Reveals which segments you are most (and least) visible to
geo	global, us, uk, de, au [etc.]	Flags prompts that need localized variants for multi-market teams
language	en, de, es, pt, ja [etc.]	Separates results by language when running multi-locale monitoring
engine	chatgpt, perplexity, google-aio, gemini, copilot [etc.]	Makes cross-engine comparison fast to run in any BI tool
prompt-family	[slug for the intent cluster, e.g. “vs-competitor-a”]	Groups variants of the same intent for citation-rate aggregation at the family level

Tools like Otterly.AI, Profound, Temso, and Peec AI all support custom prompt lists with some form of tagging or grouping. The exact implementation varies by platform: Profound uses prompt folders and engine filters; Peec AI applies labels with multi-select; Otterly.AI groups prompts by campaign. Map these tags to whatever your tool supports, then export to a spreadsheet if you need the full schema for internal reporting.

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Quarterly-refresh checklist

Run this review every 90 days. It takes about 30 minutes and prevents your prompt set from drifting out of sync with how buyers actually phrase questions today.

Audit existing prompts:

• Do the phrasing and terminology still match current buyer language? (Check Profound Prompt Volumes or Otterly.AI’s prompt research feature for demand signals.)
• Are there prompts where your share of voice has been above 90% for at least eight consecutive weeks? Retire those and reallocate the run budget.
• Are there prompts producing zero results across all engines for more than 12 weeks? Rewrite the phrasing or retire.

Add new prompts:

• Did you launch a new product feature, integration, or pricing tier in the last quarter? Add three to five decision-stage prompts covering it.
• Did a new competitor enter or a major competitor reposition? Add their name to relevant comparison prompts.
• Did buyers start using new terminology for your category? Add two to three awareness-stage prompts using that language.

Check coverage gaps:

• Run a persona audit: are all your primary buyer segments represented in the persona tags? If enterprise buyers are untagged, you cannot measure your enterprise-stage share of voice separately.
• Check geo coverage: if you are expanding into a new market, add localized prompt variants before you launch campaigns there, not after.

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Connecting the prompt set to a monitoring platform

Once your 60 prompts are adapted and tagged, load them into a platform that runs them on a schedule across multiple engines. The tools below each accept a custom prompt list:

• Otterly.AI (/tools/otterly-ai): prompt-level citation tracking across six platforms; GEO Audit Engine adds on-page recommendations alongside your share-of-voice data. Good starting point for teams building a prompt set for the first time.
• Profound (/tools/profound): the deepest citation intelligence in the category, with Prompt Volumes that show real-user demand behind your prompt choices. Best fit if you need to validate that your prompt set reflects actual buyer search behavior.
• Peec AI (/tools/peec-ai): daily tracking across nine or more engines with unlimited user seats. Well-suited to agency teams running prompt sets across multiple client brands simultaneously.
• Temso (/tools/temso): an all-in-one AI SEO platform that tracks share of voice, brand mentions, citations, and sentiment across eight engines from $89/mo. Its onboarding flow helps you set up a prompt list quickly and connects monitoring directly to an execution queue for closing visibility gaps.

See the full ranked comparison at /rankings/ai-visibility-tools or the methodology behind the scoring at /methodology. The /glossary defines share of voice, citation rate, prompt family, and the other terms used throughout this template.

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Start with the 60 prompts, then tune

Load this template, adapt the bracketed slots to your real category and competitors, apply the tags, and run the set across at least ChatGPT, Perplexity, and Google AI Overviews for a baseline. Your first week of data will already show you where the gaps are.

Then use the quarterly-refresh checklist to keep the set current. A prompt set that is 12 months out of date is not measuring your buyers. It is measuring a category that no longer exists in quite the same form.

The /rankings/ai-visibility-tools page lists the tools that can run this set at scale. Pick the one that matches your budget and team size, load these 60 prompts, and you will have a monitoring corpus that actually reflects how buyers research your category in 2026.