Quick answer

To conduct AI search prompt research, map buyer questions across three awareness stages (Problem Unaware, Problem Aware, and Solution Aware), source real phrasing from sales call notes, support tickets, and Search Console queries rather than keyword volume alone, and weight your tracking panel toward evaluation and comparison prompts.

1. Start With Buyer Awareness Stages, Not Keyword Volume

Most conversational buyer prompts show zero monthly search volume in legacy SEO tools, even when buyers ask them daily inside ChatGPT or Perplexity. Organize your prompt universe around three awareness stages first:

Wide three-stage prompt research map: Problem Unaware (Informational), Problem Aware (Jobs-to-Be-Done and Workflows), and Solution Aware (Category, Comparison, Proof)
Structure prompts across Problem Unaware, Problem Aware, and Solution Aware intent buckets before running trackers.
Awareness StageSub-Intent BucketsExample Prompt Templates
Problem UnawareInformational definitions & causes'What is [concept]?' / 'What causes [problem]?'
Problem AwareJobs-to-be-done & workflows'Best way to [process]' / 'How to automate [task]'
Solution AwareCategory, comparison, proof, setup'Best tools for [use case]' / '[Brand A] vs [Brand B]'

2. Where to Source Authentic Buyer Phrasing

Synthetic prompts invented in a brainstorm rarely match how real buyers phrase constraints like team size, compliance, or budget. Pull raw questions from four internal sources:

Vertical four-source prompt mining stack: Sales Call Transcripts, Support and Onboarding Tickets, Internal Site Search, and Search Console Long-Tail Queries
Mine sales transcripts, support logs, and site search to capture constraints buyers actually type into LLMs.
  1. Sales call transcripts and CRM objection notes ('Does it integrate with X?', 'How does pricing scale past 50 seats?').
  2. Support and onboarding tickets revealing setup, migration, and compatibility worries.
  3. Docs and help-center site search logs showing exact wording before buyers convert.
  4. Google Search Console multi-word conversational queries and AI prompt-volume tools for category discovery.

3. Weighting and Locking Your Tracking Prompt Set

For industry research, equal splits across awareness stages make sense. For brand visibility tracking, weight your panel heavier near purchase decisions while keeping the list locked across quarters:

Medium portfolio mix chart showing 20% Problem-Aware Workflow prompts, 55% Category and Comparison prompts, and 25% Brand Proof and Integration prompts
Weight commercial tracking panels toward category, comparison, and integration prompts where vendor shortlists form.
  • Keep 50% to 60% on Solution Aware category, alternative, and comparison prompts where shortlists are built.
  • Allocate 20% to 25% to Problem Aware job-to-be-done workflows where early authority forms.
  • Reserve 15% to 25% for direct brand accuracy, pricing, and integration proof checks.

Once your prompt panel is built, review our prompt selection guide and AI visibility tracking workflow.

Frequently asked questions

Why shouldn't I build an AI prompt list purely from Google keyword volume?

Conversational prompts are longer and include context like team size, industry, or budget, so most show zero search volume in traditional keyword databases despite driving real buyer research.

How many prompts should a B2B or SaaS team track?

Start with 25 to 50 well-structured prompts per product line, weighted toward unbranded category, use-case, and competitor-comparison queries.

Should I include branded prompts in my AI Share of Voice score?

Keep direct branded prompts in a separate accuracy/sentiment bucket so they do not artificially inflate your unbranded category discovery Share of Voice.

Next steps & related pages

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