Synthetic testing with AI personas

Create AI-generated respondent personas and run them through your survey before launch.

Synthetic testing lets you create AI personas with specific demographics, attitudes, and backgrounds, then run them through your survey to pressure-test question flow, logic, and quality before real respondents see it.

testingqualityaisynthetic10-20 minutesIntermediateResearchersSurvey designersQA teams

Steps

  1. Start a synthetic test
    In the survey editor Build tab, click the Quick Test button to run a fast synthetic test. You can also manage personas from the Audiences page and run them against any survey.
    Start a synthetic test
  2. Create personas
    Create personas in three ways: AI-generated (describe the persona and AI fills in details), from a template (pre-built demographic profiles), or manual (specify every detail yourself). Personas are saved to your Audiences library for reuse across surveys.
  3. Configure and run
    Select which personas to run, set the number of responses, and start the test. Optionally enable quota sampling to enforce demographic quotas across your persona pool. You can also run tests through different AI models to compare output quality.
  4. Review synthetic responses
    Synthetic responses appear in the Results tab with a "synthetic" label. Compare how different personas answered to identify question issues, biased wording, or logic problems.

Synthetic testing is like a dress rehearsal for your survey. AI personas simulate real respondents, helping you catch issues before launch.

Personas can be created with AI assistance: just describe "a 35-year-old marketing manager who is skeptical of new tools" and the AI generates a complete persona profile.

Pre-built persona templates cover common demographics and archetypes, making it easy to test with a diverse set of simulated respondents.

Personas are stored in your Audiences library and can be reused across multiple surveys, saving setup time for recurring studies.

Quota sampling lets you enforce demographic targets (e.g., 50% female, 30% age 25-34) across your synthetic respondent pool for representative results.

Multi-model testing lets you run the same personas through different AI models to compare response quality and behavior.

Run history tracks all synthetic test runs, so you can compare results across iterations as you refine your survey.

QuestionPunk