How Jev changes the model interface

How Jev changes the model interface Large language models are autoregressive. They create output one token at a time, which is good for generating text and code. But that flexibility is unnecessary if you only want to decide whether a support ticket belongs to billing, sales or technical support. Jev handles these kinds of situations. You provide it with the application state, questions and possible answers. It returns typed values with probability and confidence information. Instead of asking an LLM to generate and then parse “This looks like a billing issue,” your application can directly receive a typed “billing” value. Jev produces its outputs in parallel instead of decoding a string token by token. TypeSafe reports 70–500 millisecond end-to-end latency, $0.042 per million input tokens and no metered output-token cost. credit @bendee983