Synthetic Data Generation

Real-world data has gaps. Rare failure modes, edge cases, and underrepresented scenarios don't show up often enough to train on reliably. Synthetic data generation fills those gaps by creating realistic training examples that never happened in the real world but behave like they could have. It's not a replacement for real annotated data. It's a supplement for the cases real data can't cover well on its own, and increasingly part of the labeling pipeline for underrepresented scenarios.

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