Jev 七大用法盘点：从浏览器自动化到检索重排
一条清单帖列出 Jev 的 Choice、Score、Noul 三类问题与 7 个典型场景，包括浏览器操作选择、模型路由、工具门控、护栏、评测、记忆过滤和检索重排。
Top 7 use cases for Jev (save now): Many apps use LLMs for simple yes/no and pick-one decisions. Yet they answer word by word, and it costs time & resources. Jev is built to make these decisions faster & cheaper. (But it can't write text/code...!) You ask 3 types of questions to Jev: - Choice: pick one answer from a list. - Score: rate something on a scale - low/medium/high. - Noul: how likely a statement is true. Jev gives a number b/w 0-1 to show how sure it is about its answer. Your code could then read probability & decide what to do next. Here are 7 top use cases for Jev: 1. Browser and computer automation: Pick next action from current page: click, type, or stop. 2. Model routing: Classify each request by complexity & sends it to right LLM. 3. Tool selection & gating: Pick tool & check tool calls before they run. 4. Guardrails & content moderation: Catch unsafe inputs & prompt injections before they reach LLM. 5. LLM & agent evaluations: Score responses & sort agent failures into categories. 6. Context compaction & memory filtering: Decide what to keep & drop from an agent’s memory. 7. Semantic search & retrieval reranking: Score retrieved passages & rank them by relevance. i.e., Jev is good for fast & repeated judgments. The bottom line: Use an LLM to generate. Use Jev for decisions around it. Anything else you'd add? 1 RT to help others understand Jev. 2 If you want to learn AI engineering, @systemdesignone is your guy.