Use Equivalence Classes to Shrink the Eval Set

The move: evaluate by slices, not by piles. A large prompt log is not an eval set. It is raw material. Equivalence partitioning turns that raw material into groups of inputs that should behave similarly. Boundary thinking then adds examples at the edge, where models often change behavior: long inputs, missing context, conflicting instructions, low-confidence retrieval, or policy-sensitive wording. This works because AI teams rarely have unlimited test time. You need a set small enough to run on every model or prompt change, but representative enough to expose meaningful regression. Named slices create that discipline. When a release fails the…

Sign up free — one personalized lesson every day, matched to your role and goals.

Already have an account? Sign in