Version Your AI Graders as Code to Catch Silent Prompt Regressions

A technical article published on DEV Community argues that AI evaluation harnesses fail silently when graders are left unversioned while test cases are regularly updated. The author warns that a passing score in such setups reflects the age of the grading logic rather than the actual health of the underlying prompt or model. To address this, the piece recommends splitting graders into structural and semantic components, each versioned explicitly alongside the golden test cases. The article includes an unexecuted Python code sketch demonstrating a runner that refuses to produce a score when grader versions and test cases are out of sync. The core argument is that without versioned graders, teams lose the equivalent of a stack trace when a model quietly stops following an instruction that still appears to be tested.