Synthetic Data Quality

Synthetic data can expand scarce datasets, protect privacy, and accelerate model development. But generated data is not automatically good data. Quality determines whether synthetic examples improve a model or quietly make it worse.

What Quality Means

• Relevance: the examples match the task you care about
• Diversity: they cover meaningful variation instead of repeating the same pattern
• Realism: they resemble the structure of real-world inputs
• Correctness: labels, answers, or actions are accurate

Main Risk

Low-quality synthetic data can create shortcut learning, amplify bias, or teach a model unrealistic patterns. This is especially risky when the synthetic set is much larger than the real one.

Best Practice

Treat synthetic data as something to curate, filter, and evaluate carefully. It works best as a supplement to strong real-world data, not a blind replacement for it.