Golden Sets: Regression Engineering for Probabilistic Systems
Summary
This piece defines golden sets as formal, versioned regression tests for probabilistic AI workflows. It covers the contract structure, required case elements, outcome classes, decision criteria, common failure modes, and a minimal implementation path, arguing that production AI should employ explicit regression gates and traceability to prevent regressions.