How to Build a Reliable LLM Evaluation Harness in Java

A technical guide outlines a structured approach to evaluating large language model (LLM) applications built in Java, addressing the challenge that LLM outputs are prose rather than fixed values checkable with simple assertions. The proposed evaluation harness has three parts: a hand-curated golden dataset of representative input-output pairs, a scoring mechanism converting each case into a pass or fail, and regression testing that fails the build when scores decline. Each golden dataset entry is designed for one of three scoring methods — exact match, keyword presence, or rubric-based LLM judging — and never a combination. The guide stresses covering common cases, past production failure scenarios, adversarial inputs, and cases where the model should appropriately refuse or hedge. Keeping the dataset small enough to complete in minutes is emphasized, since a slow harness risks being skipped and losing its effectiveness.