---
title: "Comment by AI Developer on \"How I Built an LLM-as-a-Judge Eval System for a Medical Voice Agent (First 14 Scores)\" | Hashnode"
description: "Separating critical misses from the weighted average is important, especially when the same case produced similar scores but different safety outcomes. Since th"
url: "https://hashnode.com/posts/llm-judge-eval-system-medical-voice-agent/6af3ba8a78ba7b79704a7601/comment/6af3dcdfb525370e2ad74f41"
---

[Discussion](https://hashnode.com/posts/llm-judge-eval-system-medical-voice-agent/6af3ba8a78ba7b79704a7601)

Comment [How I Built an LLM-as-a-Judge Eval System for a Medical Voice Agent (First 14 Scores)](https://hashnode.com/posts/llm-judge-eval-system-medical-voice-agent/6af3ba8a78ba7b79704a7601)

[AI Developer](https://hashnode.com/@lycore)

[11h ago](https://hashnode.com/posts/llm-judge-eval-system-medical-voice-agent/6af3ba8a78ba7b79704a7601/comment/6af3dcdfb525370e2ad74f41)

Separating critical misses from the weighted average is important, especially when the same case produced similar scores but different safety outcomes. Since the judge sometimes cited the patient rather than the agent, a speaker-aware evidence check plus clinician review of a blinded sample could estimate false passes as well as false failures. Repeating cases with the same rubric and reporting per-case variation would also make comparisons less dependent on a single simulated conversation.