Llm as a judge framework
A practical answer grounded in a runnable, published harness rather than opinion. Links to Can you trust the model that grades your content? Measuring when an AI judge waves through broken work.
LLM as a judge framework. I test whether the AI helps students learn, not whether the model just sounds plausible. My education-domain judgment comes first. Then I use runnable AI-quality harnesses to make that judgment measurable. This page walks through the method, and links to one you can point at your own system.
This is the method behind Can you trust the model that grades your content? Measuring when an AI judge waves through broken work. You can read it, and you can run it.
1. Make the failure observable.
Pick the specific way this can go wrong, and build the smallest input that triggers it. If you can’t make it fail on purpose, you can’t prove it works. A worked example: Measuring feedback integrity: a blind-solver that catches AI explanations leaking the answer.
2. Measure against a baseline, not a vibe.
Compare to a neutral control so the number means something. A score with nothing to compare it to is theater. A worked example: Same answer, different grade: measuring when an AI grader can’t hold a verdict.
3. Check it a second, independent way.
Re-run with a different model family, or a held-out set. Agreement across independent checks is the only verdict worth trusting. A worked example: Measuring feedback integrity: a blind-solver that catches AI explanations leaking the answer.
The overlap is the point. I bring deep classroom, curriculum, and assessment judgment, and I make it measurable with AI/eval tooling. Every claim here maps to a public, reproducible harness, not a slide. If you want this run against your own system, the method transfers directly.