Scoring rubrics covering accuracy, compliance, tone, completeness, next-step correctness, escalation logic, and hallucination avoidance are created. Each benchmark needs to have objective pass/fail and scoring thresholds.

For every benchmark scenario, a SME-written ideal responses or step-by-step resolution is included. These serve as the reference outputs that the model is evaluated against during fine-tuning and regression testing.

As a client's domain-specific data, SOPs, pricing, messaging, or compliance rules evolve, we refresh the benchmark set to make sure the AI is tested against the current, accurate version of the domain.