Infra · AI System Designstaff

Synthetic Data Generation & Curation

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// brief

Synthetic Data Generation & Curation

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

Key Requirements

01Diversity engineering (taxonomy coverage, persona/param variation)
02Quality filtering with verifiers where checkable + calibrated judge
03Contamination checks against eval/benchmark sets (~0)
04Dedup; model-collapse awareness (mix in real data)
05Provenance/versioning; downstream improvement as the true metric

Rubric must-haves

0/3

Has a generation (model) step
Has a quality/eval filtering stage
Has an embedding/vector step for dedup/contamination

These checks are the structure score, 30% of your total. They update as you draw.

Practise it like the real round

60-minute timer paced through requirements, design and deep dive. The follow-up questions stay hidden until the last phase, and solution hints are off.

AI Review

0/5

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