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Synthetic Data Generation & Curation

Synthetic Data Generation & Curation

Design a pipeline that generates synthetic training/eval data at scale — diverse, high-quality, and uncontaminated.

Key Requirements

  • •Diversity engineering (taxonomy coverage, persona/param variation)
  • •Quality filtering with verifiers where checkable + calibrated judge
  • •Contamination checks against eval/benchmark sets (~0)
  • •Dedup; model-collapse awareness (mix in real data)
  • •Provenance/versioning; downstream improvement as the true metric

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