JEV 101：决策模型和 LLM 的分工科普
一条视频讲清 Jev 不是 LLM，而是吃结构化输入、回答带类型的问题、返回可用概率的决策模型，覆盖 Choice/Score/Null/置信度阈值和并行评估。
JEV 101 - Explained! JEV is not another LLM. It is a decision model built to take structured inputs, answer typed questions, and return calibrated probabilities your code can actually use. Here’s the simplest way to understand it: → State - Give JEV the situation as text, JSON, or structured context. → Questions - Define exactly what you want to decide: intent, urgency, department, risk, escalation, and more. → Choice - Pick one option from a predefined set and return probabilities for each. → Score - Measure where something sits on an ordered scale instead of forcing it into a simple label. → Null - Ask a yes/no style question and get a calibrated probability from 0 to 1. → Confidence - Use thresholds to decide whether to act, review, or escalate to a human. → Parallel Evaluation - Ask multiple questions in one request instead of chaining decisions one by one. → Where JEV fits - Use it for routing, triage, tool-call gating, scoring, lead qualification, or verifying LLM outputs. The key distinction is simple: JEV decides. Your code acts. The LLM writes. That means you do not have to use an expensive generative model for every small classification or routing decision. The strongest systems will likely combine both: Decision models for fast, structured judgment. LLMs for generation, explanation, and complex reasoning. That combination can make AI workflows faster, cheaper, and easier to control.