Why it matters
Hrishi Olickel describes evolving extraction schemas around the decisions a dataset must support. Designer, worker, and optimizer stages propose definitions, process records, and revise failures. Typed outputs and model agreement help organize checks, but shared errors in dates, signs, or labels can survive them. The private pipeline illustrates a workflow; its cost and performance claims are not independently verified.
My takeaway: Create held-out records with ambiguous dates, negative amounts, and misleading labels. Check outputs against independently prepared ground truth, sampling agreements as well as disagreements. Version schema changes, bound access and compute, and rerun the held-out set before promotion; retain a checkpoint that can be restored after regressions.