Why it is written this way
If you simply ask an AI to write survey questions, it will immediately generate a generic list. It is common to paste ten AI-generated questions into Google Forms, run the survey, and only realize afterward that none of the responses actually answer what you needed to know. Because you cannot easily re-run a live survey, this mistake is especially costly.
Setting the role as a researcher who "designs questionnaires and analyzes the data" is deliberate. Only those who analyze results understand which questions end up useless once data is collected. The third paragraph is the core of this prompt: breaking the objective down into core inquiries first ensures that irrelevant filler questions are eliminated.
In the format, the final column—"Target Core Inquiry Question"—makes the logical link explicit. If any row is missing this link, it can be deleted immediately. The constraints paragraph proactively prevents three common survey design flaws: leading questions, double-barreled questions, and overlapping/incomplete answer options. Excluding personal info avoids unneeded PII collection and protects completion rates.
The self-review in the final paragraph adds a layer of quality control. Prompting the AI to evaluate its own draft against three checks leads it to catch missing angles and adjust the table automatically. This verification is crucial even for familiar formats like satisfaction surveys, where generic flaws often slip through unnoticed.
Unfamiliar terms? See Aha AI: output-format, hallucination
Compared with a bad example
Make a 10-question satisfaction survey for pilates members so I can put it in Google Forms.
This produces a generic survey asking for 5-point ratings on facilities, instructors, pricing, and cleanliness. The issue is that even after running it, you still won't know why members hesitate to renew. Satisfaction scores are often generally high, and even if an item scores lower, you won't know if that is the actual reason members leave. You would have to run another survey from scratch.
Variations
When questions are ready and only ordering is needed
I have prepared a draft set of survey questions targeting {{respondent target}}. The research goal is "{{survey purpose}}".
Please organize the optimal sequence for these questions. Start with easy-to-answer items, place sensitive or high-effort questions toward the end, and make sure earlier questions do not bias responses to later ones. For each question whose order you change, provide a one-line explanation of why you placed it there.
Question sequencing alone heavily influences response rates and data quality. Requiring rationale for each placement lets you make informed adjustments.
Pre-defining output charts before drafting questions
I want to conduct a {{item count}} survey targeting {{respondent target}}. The goal is "{{survey purpose}}".
Before drafting the questions, outline the specific summary tables and cross-tabulation comparisons I should expect to see once all responses are collected. Then, work backward to specify the exact survey questions needed to populate those tables.
Visualizing the final output dashboard first exposes useless questions immediately and prevents collecting data that cannot be effectively analyzed.
Model notes
Different LLMs may use slightly varying phrasing for 5-point Likert scale options. After receiving the table, adding a quick follow-up prompt like "Standardize the 5-point scale labels consistently across all rating items" makes it much easier to copy and paste into Google Forms or Typeform.
Related prompts
Last updated 2026-09-02 · Found a mistake? Let us know