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Anthropic
S18
HardPremiumDesign a Synthetic Data Generation & Curation Agent
Design a pipeline that generates high-quality synthetic training data at scale — for fine-tuning models, building evaluation sets, or augmenting sparse datasets.
DataEvaluationTrainingQuality
Key Requirements
- Control data diversity, quality, and distribution
- Detect and remove duplicates and near-duplicates
- Validate that synthetic data actually improves model performance
- Prevent model collapse from training on synthetic outputs
- Scale generation to millions of examples efficiently
Interviewer Follow-ups
- Q1How do you prevent model collapse from training on synthetic data?
- Q2How do you validate that generated examples are realistic?
- Q3How do you ensure diversity across edge cases?