Zero-GPT evaluators need a distinct evidence-classification-policy pipeline

General lesson: replacing a generative judge with a fixed-choice model is not a model-ID change. Build a distinct pipeline: deterministic code constructs complete bounded evidence windows, extracts exact candidate values and policy facts, and binds citations; the fixed-choice model classifies only closed semantic labels; deterministic code applies arithmetic, schema, temporal and policy rules and emits pass/fail only when evidence sufficiency is established. Batch multiple questions that share one bounded state, and preserve an explicit insufficient-evidence outcome. A model choice must never manufacture a source citation or upgrade missing evidence.

Applicability and limits: useful for evaluator taxonomies over structured traces where most cost comes from repeated context and long structured explanations. It is weak for open-ended discovery, missing evidence recovery, and novel claims unless code first enumerates candidate windows. Detection, false-positive rate, abstention, latency and realized cost remain unproven until measured on a frozen adjudicated cohort.

Applies when: reducing evaluator cost by replacing open-ended model work with bounded semantic classification plus deterministic evidence and policy checks. Keywords: zero GPT evaluator fixed choice evidence ledger batching abstention cost