Investigating the detailed questions, multi-part constraints, and tasks people bring to AI systems to inform content planning.
Prompt research investigates the questions and tasks people bring to AI systems. It can include the constraints, comparisons and follow-up requests that make a conversation more specific than a short keyword. The aim is to understand needs, not invent a large list of flattering questions that only make the business look good.
Useful inputs can come from genuine customer inquiries, support conversations and documented research. Hypothetical prompts can help test coverage, but they should be labeled as constructed examples rather than presented as measured demand. The fact that a researcher can write a question does not establish how often anyone asks it.
For content planning, look at what information the task requires and whether your site can responsibly provide it. A detailed purchasing prompt may reveal missing specifications or limitations. It does not automatically justify a separate page for every possible combination of budget, location and preference.
Takeaway: Use prompt research to uncover useful information gaps and realistic task constraints. Keep observed customer needs separate from synthetic test prompts and unverified demand claims.
Example
A commercial kitchen supplier reviews repeated inquiries about doorway clearance, power supply and cleaning requirements. It builds test prompts from those real concerns and checks whether its existing product information answers them. It does not claim the constructed prompt list represents every buyer or a measured share of the market.
What to check
Record where each research question came from and remove personal details from customer material. Group prompts that need the same underlying answer. Prioritize gaps you can fill accurately instead of publishing repetitive pages designed only to match wording variations.
For more detail, see Google AI feature guidance.