Synthetic data is text, images, or labels generated by an existing model — typically a frontier model — to train, fine-tune, or evaluate another model. It's how nearly every recent open-source model leveled up: a smaller model is trained on examples produced by a larger one.
Where it shines: scarce-data domains (compliance, niche languages), eval expansion (generate adversarial cases), and distillation. Where it doesn't: anything where the teacher model has biases or blind spots — those propagate. Always mix synthetic with a smaller set of real, human-validated data.
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