Screening LLM Judges Without Ground Truth: A Preliminary Evaluation of an AI Dental Hygiene Coach
Evaluating a fine-tuned coaching model with expert graders is slow and expensive, and an LLM judge cannot be trusted without ground truth. We present a staged pipeline that screens candidate LLM judges and student models before expert review, using explicit decision rules at each stage: judge output audit, similarity with hosted judges, bias checks, and decision robustness. We demonstrate it on an AI dental hygiene coach. The case study uses synthetic, clinically unvalidated training and reference data to demonstrate the operation of the pipeline, rather than to establish the clinical performance of the resulting models. Two fine-tuned small language models (Granite-4.1-8B and Qwen3-8B), each trained at three data sizes, were scored on seven metrics by five judges (three local, two hosted). Two local judges, Qwen3.8-27B and Gemma-4-31B-it, were not distinguishable from the agreement between the hosted judges on six of seven metrics by rank correlation, and Qwen3.8-27B was the most similar to the hosted judges overall. No judge met our criterion for favoring its own model family or training-data source. The student comparison did not separate the two models on any metric under the pipeline's rule, so the choice of Granite-4.1-8B, which led in 29 of 35 judge-by-metric cells, is a lean that depends partly on truncated Qwen3-8B outputs. The pipeline also surfaced problems that raw scores hid, including the truncated outputs and hallucination flags from one hosted judge that no other judge corroborated. No dental hygiene expert has graded these responses, so the findings are preliminary and the pipeline's final stage, anchoring to expert judgement, is proposed but not performed.