Jev vs LLM-as-a-Judge

Most teams grade their agent’s outputs by handing them to an LLM and asking it to write a verdict. That pattern is called LLM-as-a-judge, and it works well enough that it has become the default. TypeSafe’s decision model, Jev, can grade the same outputs, and it does so in a different way that changes what you can do with the result. This post explains the difference, shows where it mattered in our measurements, and says which judge to use for which kind of rubric. It assumes you know what LLM-as-a-judge is. If not, read LLM-as-a-Judge: Score AI Agent Outputs Automatically first. An LLM judge writes a verdict. Jev gives a probability for a typed question. When the rubric is closed and the evidence is included in the request, Jev matched the LLM judge on accuracy. Jev has better-calibrated probabilities, is 1/5 as expensive, and has 1/10 the latency of the LLM judge. When the rubric is open and reading a whole article is required, the LLM judge follows expert ratings much more closely. Use Jev for closed criteria if the evidence is present, i.e., the answer is source-supported, on-policy, or matching a reference. It can also be used if a…