Choosing and Calibrating Evaluator Strategies
Explore how to choose appropriate evaluator types such as deterministic checks, rubric scoring, human review, and model-graded evaluation to measure AI system quality. Understand calibration methods that align evaluators with service level objectives, ensure auditability, and manage risks like bias, drift, and scaling costs. Develop a mixed evaluator strategy optimized for accuracy and operational efficiency for enterprise AI release gates.
A golden dataset supplies the fixed inputs and expected outputs. It doesn’t decide who or what is allowed to grade a response against them. That choice determines whether the result counts as evidence at all. Decide which evaluator types count as acceptable evidence for each quality SLO and release gate, and which only provide supporting signal.
That decision starts with a boundary between measurement and judgment. Deterministic checks measure an invariant that either holds or fails: JSON schema validity, required fields present, tool call arguments parseable, citations included when required, a retrieval result set containing at least one source from an allowed domain list. Rubric scoring formalizes judgment for a criterion that stays valid even when multiple answers could be acceptable: helpfulness, adequacy, safety nuance in a borderline case. Human review performs judgment directly. Model-graded evaluation performs judgment through a probabilistic proxy. It only becomes release-grade evidence once calibration shows it tracks human labels within an agreed error tolerance for the specific task slice.
The golden dataset plan from the last lesson provides the controlled inputs and reference outputs needed to apply these evaluator types consistently. The quality SLOs from two lessons ago define the attributes each evaluator must measure. Each SLO can usually be broken into a small set of deterministic checks plus one or more quality scores that require evaluator judgment. The evaluator strategy ties those components to evidence that can be produced within the engineering gate and traced back to the underlying evaluation record.
Evaluator options and predictable failure modes
Deterministic checks provide crisp, auditable pass/fail evidence. They fit best where the requirement ...