Core

Synthetic Data Generation

Synthetic data uses a strong model to manufacture training or evaluation examples when human labels are scarce or expensive: instruction/response pairs, hard edge cases, distillation targets. It works when you bolt on real quality controls (dedup, filtering, diversity, verification) and fails quietly when you skip them, because you can poison your own training set and contaminate your own benchmarks.

a free account unlocks the core curriculum tier · no card

RELATED CONCEPTS

Golden Datasets and Eval Sets Benchmarks and Their Limits Knowledge Distillation

PRACTICE THIS IN REAL QUESTIONS

LLM & GenAI FundamentalsThe customer's LLM bill is unsustainable. Cut costs 10x without destroying quality. Walk me through it.→LLM & GenAI FundamentalsYou have a fixed latency and cost budget at scale. Walk me through quantization vs distillation to hit it.→Machine Learning & Data ScienceBuild a 10M-sample instruction-tuning dataset from 100B web docs. Design the pipeline.→LLM & GenAI FundamentalsClassify 100M product descriptions with an LLM when the budget covers a fraction. Design the pipeline.→