How to Create AI Judge Metrics You Can Trust
Blog post from Coval
In Coval, human review is utilized to calibrate LLM-as-a-judge metrics, ensuring accurate evaluation of AI agents by validating the AI's scoring with human oversight. By having a QA team label a small sample of conversations and identify discrepancies between human and AI assessments, the calibration loop is established: labeling conversations, checking agreement rates, inspecting disagreements, fixing metric prompts, and re-testing calls. This process helps uncover issues like ambiguous prompts or incorrect AI judgments, enabling teams to refine metrics without altering the AI model or agent behavior. For example, human review revealed that a supposed compliance issue in an outbound voice agent was due to an incomplete metric prompt rather than actual agent failure. By refining this prompt, the AI's judgments aligned more closely with human assessments, reducing the need for continuous human oversight as the system's reliability improved. Coval also offers a service for teams to outsource this calibration process, ensuring metrics accurately reflect agent performance and support informed decision-making.
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