Synthetic Users: If, When, and How to Use AI-Generated “Research”

What Are Synthetic Users?

• Definition: Synthetic users are AI-generated profiles that mimic user groups, providing artificial research findings without studying real users.
• Characteristics: They express simulated thoughts, needs, and experiences, based on large language models (LLMs) trained on vast amounts of data about people.

Use Cases for Synthetic Users

• Limited Use: There are a few circumstances where synthetic users might be useful, such as when real users are inaccessible or when quick insights are needed.
• Risk: Relying solely on synthetic users can lead to biased or inaccurate research outcomes, as they lack the authenticity of real user input.

Importance of Real User Research

• Critical Role: Real users provide invaluable insights, emotions, and behaviors that cannot be replicated by AI-generated users.
• Validation: Authentic user research helps in understanding genuine user needs, preferences, and pain points for product development.

Guidelines for Using Synthetic Users

• Supplementary Tool: Consider using synthetic users as a supplement, not a replacement, to traditional user research methods.
• Cross-Validation: Validate findings from synthetic users by incorporating real user feedback, ensuring more accurate and reliable results.

Conclusion

• Balanced Approach: While synthetic users have some utility, real user research remains essential for comprehensive and reliable insights into user behavior and preferences.
• Ethical Considerations: Exercise caution and transparency when using AI-generated data to ensure the integrity and validity of research findings.