← Back to Glossary
AI and Machine LearningS

Synthetic Users

Definition

Synthetic users are AI-generated simulations of user personas, created by prompting large language models with detailed demographic profiles, behavioral characteristics, goals, and context. These simulated users can respond to survey questions, participate in mock interviews, react to product concepts, or provide feedback on designs, producing outputs that resemble real user research data.

The approach emerged from the broader synthetic data movement but carries distinct implications for product and design teams. While synthetic data generates training examples for machine learning models, synthetic users generate research-like insights for product decisions -- a higher-stakes application where the limitations of AI-generated responses become more consequential.

Why It Matters for Product Managers

Synthetic users address a real constraint: getting access to real users for research is expensive, slow, and sometimes impossible in early product stages. Generating synthetic user perspectives in minutes rather than weeks is genuinely useful for tasks like pre-testing survey instruments, stress-testing personas, exploring edge cases, and generating initial hypotheses.

However, the risk of misuse is significant. PMs who treat synthetic user responses as equivalent to real research data will make decisions based on AI-generated patterns rather than genuine human needs. This is especially dangerous because synthetic responses often sound plausible and articulate, creating false confidence in insights that may not reflect reality.

How It Works in Practice

  • Define detailed persona profiles -- Create rich descriptions including demographics, job role, tech savviness, goals, frustrations, and behavioral patterns. The more specific the persona, the more useful the synthetic responses.
  • Simulate specific research activities -- Use the synthetic personas for targeted tasks: "Review this onboarding flow and identify confusing steps" or "Answer this 10-question usability survey."
  • Use outputs to generate hypotheses -- Treat synthetic responses as hypothesis generators, not conclusions. They reveal what patterns the AI has learned from similar users, which may suggest real issues worth investigating.
  • Validate all findings with real users -- Every insight from synthetic users must be confirmed through actual user research before informing major product decisions.
  • Document research provenance -- Clearly label which insights came from synthetic users versus real research, so decision-makers understand the confidence level of each finding.

    • Common Pitfalls

  • Treating synthetic user responses as real data, leading to product decisions based on AI-generated patterns rather than actual human behavior.
  • Using synthetic users for validation when they can only generate hypotheses. A synthetic user cannot tell you whether a real person would actually pay for a feature.
  • Homogeneity in synthetic responses -- LLMs have biases toward certain perspectives and often generate responses that converge on similar themes, missing the diversity of real user populations.
  • Skipping real user research because synthetic research is faster and cheaper, creating a dangerous gap between product assumptions and user reality.

    • Related Concepts

    Synthetic Users are an emerging application of Large Language Models within AI UX Design research workflows. Their outputs can contain Hallucination -- confident but incorrect claims about how users would behave. Red Teaming practices help identify where synthetic user outputs are unreliable, and Human-AI Interaction research guides how to design the interface between human researchers and synthetic user tools.

    Related Terms

    AI UX DesignHallucinationLarge Language Model (LLM)Red-TeamingHuman-AI Interaction

    Frequently Asked Questions

    What are synthetic users in product management?+
    Synthetic users are AI-generated simulations of user personas that can respond to surveys, participate in simulated interviews, or provide feedback on designs. They are created by prompting LLMs with detailed persona descriptions, behavioral traits, and context. Product managers use synthetic users to supplement (never replace) real user research -- for example, to stress-test a survey before sending it to real users, or to quickly generate diverse perspectives on a concept.
    Can synthetic users replace real user research?+
    No. Synthetic users cannot replace real user research because they generate responses based on patterns in training data, not genuine human experience. They cannot surface truly novel insights, emotional reactions, or edge cases that real users encounter. They are best used as a supplement: pre-testing research instruments, generating hypotheses to validate with real users, and filling gaps in early-stage ideation when access to real users is limited.
    ← PreviousSynthetic DataNext →Technical Debt

    Explore More PM Terms

    Browse our complete glossary of 100+ product management terms.

    Full GlossaryExplore Frameworks