Gustavo Campelo explains Jev’s decision outputs and TypeSafe’s stated RLCD training objective. A live support-ticket experiment in the playground demonstrates the request structure, returned probabilities, latency, and token consumption.
Original by Gustavo Campelo - DesenvolvedorClassificationBeginner12 min 33 secPublished Source reviewed
Before you press play
What you’ll find in the video
Jev functions as a specialized System 1 decision model choosing from predefined discrete categories rather than generating open-ended chat text.
TypeSafe reports training Jev via RLCD to produce confidence scores that facilitate routing uncertain cases to human verification.
Testing in the official playground demonstrates configuring context, questions, and discrete choices to route support requests with sub-second execution.
Worth knowing
Gemini-assisted video/transcript review. Zero hallucination claims only reflect rigid adherence to user-provided choice sets, which does not prevent classification errors or misrouted queries.