ThinkWithPrompt by SynotticThinkWithPrompt

Write a screener to recruit the right research participants

Builds a short screener survey that finds the right people for interviews or tests and filters out poor fits and professional testers.

Product & UX DesignFew-ShotStandardHR & HiringResearchWriteFill-in Template

The prompt

Write a screener questionnaire to recruit participants for my research. Study: [interviews / usability test / diary study; topic] Who I need: [TARGET PROFILE - behaviors, tools used, frequency, role, region] Who I must exclude: [e.g. employees, competitors, UX professionals, recent participants] Mix I want: [e.g. half new users, half power users; device split] Number of participants: [e.g. 6-8] Recruitment channel: [panel, our customers, social, recruiter] Rules: - Screen on behaviors and recent actions ("In the past month, how often did you..."), not self-ratings or intentions. - Do not reveal the right answer: use multiple-choice options with plausible distractors and avoid naming our product early. - Put disqualifying questions first so people exit quickly. - Include one open-ended articulation question to check people can explain their thinking. - Collect only the personal data needed; ask demographics only if they matter for the mix, with "prefer not to say" options, and explain how data will be used. - Keep it under 10 questions and about 3 minutes. Return: the screener with each question, answer options, and the qualify/disqualify logic marked; quotas for the mix; a short invitation message with incentive [ADD AMOUNT], time needed, and format; and a scheduling confirmation message.

Highlighted [brackets] are blanks to replace with your own details.

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What to fill in

  • interviews / usability test / diary study; topic
  • e.g. half new users, half power users; device split
  • e.g. 6-8
  • panel, our customers, social, recruiter
  • ADD AMOUNT

How this prompt is built

This prompt follows the Few-Shot structure (Few-shot prompting). A few-shot prompt includes one or more worked examples of the input and the output you want, so the model can copy the pattern instead of guessing it.

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