What Is Prompt Coverage? Building an AI Visibility Question Set
Prompt coverage describes how well your AI visibility questions represent customer needs. A hundred variations of one question may cover little ground. Ten distinct questions could represent discovery, comparison and buying decisions more effectively. The count alone does not establish coverage.
This is the sampling layer of the SI visibility guide. A poorly chosen question set can produce mathematically correct metrics that support the wrong decision.
Start with customer decisions
Write down what you sell and what the buyer needs to decide. For a small hotel, reservations, guest messages and migration from spreadsheets are distinct needs. “Best hotel software” cannot represent all of them.
| Stage | Illustrative question | Need represented |
|---|---|---|
| Discovery | How can a small hotel organize reservations? | Problem and solution types |
| Comparison | Should I use Sheets or a custom application? | Trade-offs and limitations |
| Suitability | Which solution supports a small Turkish-speaking team? | Market, language and team size |
| Buying | How do I migrate existing booking records? | Implementation and transition |
| Brand check | What services does [Brand] provide? | Accuracy of brand information |
These are sample questions, not observed visibility results. Use customer interviews, search data and actual proposal questions to build your set. Keep private customer information out of public prompts.
Separate branded and unbranded questions
A question containing your name naturally makes a mention more likely. Such questions help test whether the information is accurate. Unbranded questions examine discovery by buyers who do not already know you. Combining both in a single mention rate may overstate discovery performance.
Languages and markets also have different decision expressions. A literal translation may change the buying intent. Measure each market separately, then compare equivalent decision groups.
Repetition and variety address different problems
Different questions broaden coverage. Repeating a question reveals answer variability under similar conditions. Three distinct questions and three repetitions of one question are different samples.
Google’s AI features documentation describes searches across related subtopics. My practical recommendation is to represent those decision subtopics. Google does not prescribe a required prompt count.
Maintain a comparable baseline
Assign an ID, decision group, language, market and priority to every question. Record product, mode, date and repetition number with each answer. Preserve the raw response and source links, and mark failed requests separately.
Keep the core set stable for trend reporting. Add questions about new services to a separate discovery set. If you revise the core set, record when and why; do not compare the new aggregate with the old aggregate without explaining the change.
How many prompts are enough?
There is no universal number. Start with the important decision groups and a set whose raw answers your team can actually review. If time or budget limits the scope, prioritize commercially relevant needs and state the limitation in the report.
For each question, you should be able to identify a relevant page or service and explain what decision the measurement could inform. If that connection is missing, remove redundant questions before expanding the set.
Next, calculate share of voice and mention rates separately. The AI search optimization service can combine this baseline with implementation.