Practical write-ups on synthetic data, evaluation and data quality, grounded in published research. Each article lists its sources.
What the 2024 Nature paper actually found, what it does not say, and the controls that follow from it.
Position, verbosity and self-preference bias, and a calibration routine that makes judge scores defensible.
Why near-duplicates quietly hurt training and evaluation, and how to remove them without losing useful variety.
The documentation that lets a buyer judge a dataset before using it, including fields specific to generated data.
Synthetic datasets and evaluation benchmarks for teams shipping LLMs and AI agents, delivered with the evidence.