This page sits in the research cluster because the prompt roster is the input set for the measurement methodology: change the roster and the measured result changes with it. Prompt selection can change the result more than another dashboard feature. The same brand can be absent for broad category questions and visible for a narrow question it answers unusually well. AirPulse's MyChild field note is one example: in a 45-prompt, four-engine run, the brand appeared in 23 of 180 response cells, concentrated in open-source and developer-intent questions.
Start with the buyer decision, not the category label
“AI visibility” can mean marketing visibility or software observability. “GEO” can mean generative engine optimization or geospatial analysis. A broad category phrase may pull the wrong market into the answer.
Write the decision first. Examples:
- choose a platform that tracks brand mentions and citations across ChatGPT, Gemini and Perplexity;
- understand why a competitor is recommended but the brand is absent;
- find a tool that connects AI visibility with Search Console and analytics;
- prove whether a content change improved answer-engine outcomes.
Then write questions a buyer could naturally ask to make that decision.
Use five evidence sources
1. Sales and customer calls
Pull exact questions from discovery, objections, procurement and renewal calls. Record the role, buyer stage and product area. Do not publish customer wording that contains names or confidential context.
2. CRM and support notes
Look for repeated pains, alternatives, requirements and “why not” questions. These often produce more useful evaluation prompts than top-of-funnel keywords.
3. Search Console and site search
Use observed queries to identify existing language and pages with demand. Search data does not reveal every AI question, but it grounds the roster in real phrasing.
4. Product and comparison pages
Convert product capabilities into buyer questions. “Citation history” becomes “Which tools show how citation sources change over time?” A capability is not a prompt until it is written as a decision question.
5. LLM and search expansion
Use ChatGPT, Gemini or another model to expand gaps after the observed evidence is organised. Label generated prompts. Review them for ambiguity, duplicates and invented demand.
Google recommends original information, clear sourcing and useful first-hand analysis in its people-first content guidance. The same principle applies to a measurement roster: generation can assist, but it should not replace evidence from buyers and the product.
Organise prompts by buyer stage
| Stage | Purpose | Example |
|---|---|---|
| Problem | Names the pain without a solution category | Why does my AI visibility score change between runs? |
| Category | Looks for an approach or class of product | How do brands measure visibility in AI answers? |
| Evaluation | Compares capabilities or criteria | Which tools repeat prompts and show citation history? |
| Vendor | Names the brand or a direct comparison | AirPulse vs HubSpot AEO |
| Proof | Asks whether an intervention worked | How do I prove that a GEO change improved citations? |
A healthy roster includes non-branded discovery and evaluation questions. Branded prompts are useful for reputation monitoring, but they cannot show whether the brand enters a shortlist before the buyer knows its name.
Add use-case and audience qualifiers only when they change the answer
Qualifiers improve relevance when they represent a real constraint: enterprise governance, B2B SaaS, healthcare compliance, local services or a specific integration. They create noise when they are inserted only to manufacture more pages.
Keep one broad parent question and a small number of decision-relevant variants. Do not create one prompt and one article for every wording change.
The MyChild case: one brand, three question neighbourhoods
AirPulse's corrected 29/100 field-note analysis shows why prompt fit matters. A later production run used 45 prompts across ChatGPT, Gemini, Google AI and Perplexity. The public aggregate download contains the grouped counts without customer identifiers, raw prompts or raw answers.
| Question group | Mention cells | Rate within group |
|---|---|---|
| Explicit open-source prompts | 12/28 | 42.9% |
| Adjacent developer prompts | 9/24 | 37.5% |
| Other child-development prompts | 2/128 | 1.6% |
The open-source/developer groups were about 33.75 times more likely than the other group to produce at least one prompt-level mention in that run. The classification was created after observing the data, involved one brand and did not preserve generated fan-out queries. Treat it as a strong descriptive example, not a universal rule.
Score each candidate question before adding it
Use a simple 0–2 review for six criteria:
| Criterion | 0 | 1 | 2 |
|---|---|---|---|
| Buyer evidence | Generated only | Indirect signal | Seen in calls, CRM or search data |
| Commercial intent | Informational only | Adjacent to decision | Directly affects selection or action |
| Product fit | AirPulse cannot help | Partial connection | Direct product workflow |
| Evidence advantage | Generic advice | Some internal examples | Original data or repeatable test |
| Wording clarity | Ambiguous category | Needs qualifier | Clear question and market |
| Measurement value | No stable outcome | Secondary diagnostic | Mention, citation or retrieval outcome |
Prioritise prompts scoring 9 or more out of 12. Keep lower-scoring questions in a backlog until better evidence appears.
Build the first roster
For a first 12–20-prompt roster:
- choose three to five business decisions;
- add one problem question per decision;
- add one category or evaluation question per decision;
- add the strongest use-case qualifier where it changes the answer;
- include two or three branded/vendor prompts for reputation context;
- remove duplicates and ambiguous category phrases;
- assign intent, buyer stage, owner and success metric;
- freeze the wording and version the roster.
Do not optimise the roster after seeing the baseline. If a new wording is genuinely better, add it as a new version and preserve the old series.
Match every prompt to an evidence page
The roster is also a content map. Every important prompt should point to one canonical page that can answer it completely. Several related questions can belong to one page.
For this AirPulse cluster:
- “How many times should I repeat an AI visibility prompt?” maps to the 30,504-response research page.
- “How should AI visibility be measured?” maps to the measurement methodology.
- “Which prompts should a brand track?” maps to this page.
- Product behaviour maps to Prompt Analysis and Prompt Visibility.
This cluster structure creates a clear internal-link path without publishing thin near-duplicates.
Review quarterly, measure continuously
Keep a frozen core roster for trend continuity. Review it quarterly, or when the product category, audience or buyer language changes materially.
During review:
- retain high-value prompts even when the brand is absent;
- retire prompts that no longer represent a buyer decision;
- add new observed questions as a new roster version;
- separate market changes from measurement changes;
- record why each prompt was added, edited or removed.
Use the AirPulse measurement methodology for run counts, storage and reporting. Use Prompt Analysis to group and review the roster inside the product.
Download the worksheet
The worksheet is a CSV template with fields for prompt, buyer stage, evidence source, commercial intent, product fit, evidence advantage, wording clarity, measurement value, total score, owner and version.