Synthetic Data
What is synthetic data?
Synthetic data is artificially generated data created by AI or other automated methods, rather than collected from real-world observations or interactions. Instead of gathering data from actual users or events, teams use AI systems to generate simulated data that mimics real-world patterns and behaviors.
In AI product development, synthetic data serves as a practical alternative when real data is scarce, expensive, or raises privacy concerns.
When should teams use synthetic data?
Synthetic data is particularly valuable in several scenarios:
Creating test datasets for evals. When building AI evaluation systems, teams often need diverse test cases to assess model performance. Synthetic data allows teams to generate edge cases, rare scenarios, or specific conditions that might be difficult to capture from real users.
Simulating user interactions. Teams can use synthetic data to model how users might behave with a new feature or product before launch, enabling faster iteration and testing without waiting for real user feedback.
Protecting user privacy. When real user data contains sensitive information, synthetic data provides a way to develop and test systems without exposing personal details.
What are the risks of synthetic data?
While synthetic data offers advantages, teams must manage it carefully. Synthetic data may not fully capture the complexity and unpredictability of real-world scenarios. There's also a risk of data leakage—if synthetic data contaminates test sets or training data, it can create misleading evaluation results.
The quality of synthetic data depends heavily on the generation method and how well it represents actual user behaviors and edge cases.
Learn more:
- How I Designed & Implemented Evals for Product Talk's Interview Coach
- AI Prototyping: How 11 Real-World Teams Are Transforming Their Work with Lovable
- Debugging AI Products: From Data Leakage to Evals with Hamel Husain
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