The synthetic data trap not talked about enough #ai #llm #productmanagement

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Homogeneous synthetic data creates a false sense of security. When your test queries lack diversity, you're essentially validating your system against a narrow slice of reality. Research shows diversity in evaluation data directly correlates with out-of-distribution generalization. The production gap widens because your system performs well on similar patterns but fails on edge cases you never tested. Quality-diversity tradeoffs exist in synthetic data generation. Most LLMs optimize for output quality, which inherently limits output diversity. This is why structured dimension-based approaches outperform naive prompting for synthetic data generation.