Excited to try Jev in parts of our systems that need calibrated probabilities for categorical decisions. We currently use a hacky version of this idea: small LLM classifiers for routing, citations, parts of Vault, tool use, and user escalation. One challenge is that LLM softmax probabilities aren’t necessarily calibrated confidence estimates. It will be interesting to see how RLCD improves calibration over the naive approach. Jev doesn’t generate text, so its “hallucination-free” framing isn’t a full solution to hallucinations. But better routing, citation selection, and escalation could reduce hallucinations across the broader system. Longer term applications for law firms include matter selection, associate staffing, and predicting billing disputes. Also excited to see open-source implementation of RLCD so we can post-train these models ourselves.