training-data

Coverage of how datasets are sourced, curated, labeled, and validated for building and fine-tuning models. Expect practical accounts of gathering prompts and examples, designing taxonomies, spotting bias and gaps that distort what the data represents, and cleaning noisy inputs. The focus stays on the messy realities behind data collection and the decisions that shape model behavior long before any training run begins.