This tutorial introduces TypeSafe's Jev decision model in Hindi. The presenter contrasts parallel option scoring against autoregressive LLM generation, walks through the TypeSafe web playground, inspects JSON scoring and resume screening schemas, and demonstrates sending requests to the REST API via a Python script.
Original by Hasan Faraz KhanGetting startedBeginner16 min 16 secPublished Source reviewed
Before you press play
What you’ll find in the video
Jev scores predefined categorical choices and questions in a single parallel pass rather than generating autoregressive tokens sequentially.
The TypeSafe playground configures evaluation tasks using context 'state' alongside questions configured with specific output types like choices, scores, or booleans.
Developers can query Jev directly over HTTP in Python by passing bearer authentication and a JSON payload containing the state, model version, and question criteria.
Worth knowing
Gemini-assisted video/transcript review. Confidence scores and structured JSON adhere to predefined schemas, but they represent model estimates and should not be assumed to guarantee factual correctness.