LLM-as-a-Judge
Using a language model to grade or classify another model's output against a rubric - a flexible but costly evaluation step, used sparingly in enforcement.
LLM-as-a-judge uses one model to evaluate another's output - scoring a response for policy compliance, quality, or safety against a stated rubric. It captures nuance that fixed rules miss, which makes it valuable for the genuinely ambiguous cases a deterministic check cannot resolve.
Its weaknesses are cost, latency, and non-determinism: sending every request to a model judge is slow and expensive, and the judge itself can be wrong or inconsistent. That is why DataStrict's adjudication layers it last - deterministic rules and classifiers settle the overwhelming majority of traffic in microseconds, and only the ambiguous remainder escalates to a model-graded review.
Govern AI like infrastructure.
Talk to our team about deploying DataStrict across your enterprise stack.