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Synthetic Data Generation
Synthetic data is training or eval data produced by a model, a simulator, or a program rather than collected from the real world, used to bootstrap labels, cover rare cases, and distill a larger model into a smaller one. Its value hinges on quality, diversity, and the absence of leakage between your generator and your eval. Applied AI interviews probe it because candidates reach for it as a free fix and miss the failure modes: distribution mismatch, eval contamination, and model collapse from training on a model's own outputs.
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LLM & GenAI FundamentalsHow and when do you use synthetic data (LLM-generated) for training or fine-tuning?→System Design for AI in ProductionDesign a text-to-SQL feature: let users ask questions in natural language over a real database.→ML Infrastructure & GPUsExplain knowledge distillation: how it works, when to use it, and how it compares to quantization and pruning.→SQL & Data EngineeringWhat data-quality checks do you put on a pipeline, and how do you catch bad data before it spreads?→SQL & Data EngineeringHow do you find and remove duplicate rows in SQL, including 'fuzzy' near-duplicates?→LLM & GenAI FundamentalsHow do you prepare a dataset to fine-tune an LLM, and why does data quality dominate?→
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