AI survey question builder
Bad survey data usually comes from bad questions, not bad respondents. Leading wording, double-barrelled items, and lopsided scales quietly push answers in a direction, and you only discover it after the responses are in. This builder produces an LLM prompt that generates questions tied to your goal and explicitly guards against the classic biases — so the survey measures what you actually want to know.
How it works
You describe the survey’s goal, the audience, the question types you want (multiple choice, Likert scale, ranking, open-ended), and how many questions. The builder writes a prompt that instructs the model to keep every question anchored to the goal, use neutral non-leading wording, avoid double-barrelled and loaded items, and build balanced, labelled response scales. It also asks the model to return a short bias check next to each question, noting why it is neutral, so you can review the reasoning rather than trust it blindly.
Common survey question failures — and how the prompt guards against them
| Failure | Example | What the prompt does |
|---|---|---|
| Leading wording | ”How satisfied were you with our excellent service?” | Instructs the model to use neutral framing with no embedded judgment |
| Double-barrelled | ”Was the product fast and easy to use?” | Requires each question to test exactly one idea |
| Acquiescence bias | Scale from “Agree” to “Strongly agree” | Enforces balanced scales with equal positive and negative points |
| Loaded assumption | ”When did you stop finding the app frustrating?” | Forbids presuppositions the respondent may not share |
| Demographic first | Asking age and income at the start | Orders sensitive questions to the end of the survey |
Worked example
Goal: understand why trial users do not convert to paid. Audience: users who created an account but did not purchase within 14 days. Question types: three Likert, two multiple choice, one open-ended. Count: six questions.
The generated prompt instructs the model to write questions targeting the decision moment — what they were trying to do, what stopped them, and what would have changed their mind — without presupposing a reason. Each Likert item gets a five-point scale with fully labelled endpoints. The open-ended item is placed last so it captures anything the closed questions missed.
Tips and examples
- One idea per question. “Was the product fast and easy to use?” is two questions. The prompt splits these automatically.
- Avoid the agreement trap. Always offer a genuine negative option; the prompt enforces balanced scales so “agree” isn’t the only easy answer.
- Put demographics last. Sensitive or boring questions at the end protect your completion rate — the prompt orders them accordingly.
- Pilot before sending. Even bias-checked questions benefit from five test respondents; the prompt suggests a pilot note where useful.
- Match question type to what you need. Likert scales measure intensity; multiple choice captures category; open-ended captures reasons. Mixing types in one survey gives you richer data than any single format alone.