Using Jev to filter context for an email agent
A thought experiment where Jev returns structured decisions in parallel, such as include_in_context, category and urgency, so only 20 of 500 emails reach the context window.
This is excellent. So much of agentic work is actually classification: routing to the right path, choosing the next action, or triaging context. Imagine an email agent with 500 new emails but room for only 20 in its context window. Before any real reasoning begins, it needs to identify messages from important people, active projects, or urgent threads. For each email, Jev could return something directly usable by code: include_in_context: true category: active_project urgency: 0.91 confidence: 0.97 Jev could be a game changer for latency and cost. Instead of generating an answer token by token, it returns typed decisions and probabilities in parallel. And it’s much more versatile than training a traditional BERT-style classifier for every task. You keep the flexibility of a prompt + runtime context, with the speed and predictable output of a classifier. Basically the programmability of a language model shaped like a classifier so it’s very cool. Congrats @CompleteSkeptic, you cooked!
This post quotes another post: “ChatGPT co-inventor releases Jev, a model built for decisions rather than text”