Exam-style
A support team generates roughly 40,000 answers a week and wants each one scored for helpfulness and tone. Human reviewers can read a few hundred a week at most. What approach scales, and stays trustworthy?
Reveal the answer
A. Score every output with a judge model against a written rubric, calibrated against a few hundred human-scored examples
A judge model scoring against an explicit rubric is the only option here that reaches 40,000 items a week. The few hundred human-scored examples are the reference baseline, showing how closely the judge tracks human ratings. Self-preference bias means a judge scores outputs resembling its own higher than a neutral grader would, and AWS answers it by picking a judge from a different provider or architecture, so scoring each answer with the model that wrote it runs that bias at its worst. Amazon Bedrock separates the two roles, a generator model and an evaluator model, and its built-in judge metrics include Helpfulness and Professional style and tone. A managed evaluation job takes at most 1,000 prompts, which makes it the calibration run rather than the weekly scoring pass. ROUGE and METEOR measure word overlap against a reference, Comprehend sentiment returns positive, negative, neutral or mixed, and three reviewers an item multiplies work already out of reach.
Q. You need to score thousands of outputs on quality without a human reading each. Approach and caveat?
A. LLM-as-a-judge: a second model scores outputs against a rubric, at scale. Amazon Bedrock separates the two roles, a generator model and an evaluator model. The caveat is self-preference bias: a judge scores outputs resembling its own higher than a neutral grader would, so pick a judge from a different model family than the one under test and check its scores against a small human-scored sample.
Why? The judge gives you scale. The human sample shows how closely its scores correlate with human ratings; AWS’s test is correlation, not exact agreement.