I Built Surveys That Get Smarter With Every Response

Using Google Gemini to generate follow-up questions based on what each person says — and what's missing from the dataset. I wrote about the concept behind Dyadem when I built it for the Gemini 3 Hackathon . Since then I've iterated on it — added an AI question authoring step, deployed it for a real community questionnaire, and learned a lot about what works. This article goes into the engineering. Most surveys ask everyone the same questions. You get shallow data and bored respondents. I built Dyadem — an open-source survey platform where AI generates follow-up questions for each respondent based on two things: what they just said, and what gaps exist across all responses so far. Early respondents get broad exploratory questions. By response 50, the AI is asking about specific things nobody's mentioned yet. Here's how it works and what I learned. The Flow User answers 3 fixed questions → App gathers dataset context (aggregate stats, themes, gaps) → Builds a prompt: this person's answer