High-volume AI scoring with Jev and Pydantic Evals

Pydantic AI and Pydantic Evals 2.46.0 integrate with TypeSafe’s Jev model to perform inexpensive structured evaluations such as policy classification, rubric scoring, and secret-request detection, with results and telemetry logged through Logfire. A support-reply example combines three independent measurements in one Jev request: a categorical policy verdict, a normalized completeness score, and the probability that a response asks for a password or login code, emphasizing that helpfulness and policy compliance can differ. The approach uses typed Python enums and Pydantic models to define evaluation questions and rubric descriptions, retains fractional score distributions and confidence data, and supports both custom multi-question evaluators and simpler built-in LLMJudge and GEval checks. Tests on eight synthetic replies identified unsafe password requests, refund promises, vague answers, and invented policy as violations while showing that concrete next steps can still be unsafe. Based on published pricing assumptions, evaluating one million replies with three recorded results each would cost about $42 for Jev inference and $20 for Logfire telemetry, compared with an estimated $4,500 marginal score-recording fee on Braintrust Pro before inference, though actual costs depend on token volume, telemetry records, retries, and retention.

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