Dataset Engineering

When building AI systems, data quality means different things at different stages. During pre-training, quality is measured by the number of tokens processed. During post-training, it shifts to the number of well-crafted examples. Three dimensions matter most when thinking about data: Quality — is the data accurate and clean? Coverage — does it span the problem space? Quantity — is there enough of it? What makes a dataset high quality? ...