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Core
Synthetic Data Generation
Synthetic data is training or eval data made by a model, a simulator, or a program instead of gathered from the real world, used to bootstrap labels, cover rare cases, and distill a larger model down into a smaller one. Whether it helps depends on quality, diversity, and keeping leakage out between your generator and your eval. AI, ML, and GenAI engineer interviews probe it because candidates grab it as a free fix and overlook the failure modes: distribution mismatch, eval contamination, and model collapse from training on a model's own outputs.
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