llm judge

Summarization is the one task where the model was handed everything it needed and still made something up. The signal is separating intrinsic from extrinsic hallucination, knowing why ROUGE cannot see either, and gating on entailment before you ship.

Letting a model write your prompts works, right up until the optimizer memorizes your eval set and hands you an unreadable prompt that scores better and generalizes worse. The bounds are the whole answer.

An answer can be correct yet still unfaithful, asserting things the retrieved context never said. The signal is scoring faithfulness against the context, not against truth, and knowing why LLM-judge faithfulness scores drift.

Asking a model 'is this answer hallucinated?' barely beats a coin flip. This is the build: claim extraction, a judge forbidden from using world knowledge, a denominator that excludes hedges, and one model call per claim, which decides where the check can run at all.