A question library is not a keyword list. Each item should represent a real decision scenario and retain its intent, value, competitive context, and next action.
- Keep one core intent per question
- Preserve the natural user phrasing
- Separate informational from decision-stage questions
Step 1: Collect decision scenarios
Start with comparisons, alternatives, pricing and proof, implementation, and risk questions. Use sales calls, search signals, support conversations, and industry discussions.
- Keep one core intent per question
- Preserve the natural user phrasing
- Separate informational from decision-stage questions
Step 2: Add categories and priority
Classify each question by topic, intent, and priority. High-priority questions often combine commercial value with competitor presence or brand absence.
Step 3: Review before monitoring
Approve the wording before adding it to answer monitoring. Start with a representative baseline set instead of a large volume of low-value prompts.
How is a question library different from keyword research?
Keywords describe matching demand. Questions retain the full prompt, intent, and answer context needed to analyze AI responses.
Should the library change over time?
Yes. New products, seasonality, category shifts, and competitor moves create new questions to review.