#Jev — Kunal Ganglani

code editor displaying react source code

Jev Models Explained: Faster Routing, Reranking, JSON

Jev-style decision models are non-autoregressive models for routing, reranking, and structured outputs. Here’s how they work, where they fail, and how to evaluate them like a builder.

Keep exploring

Close to this topic, not on this page.

AI Agent or Workflow? How to Tell Which You Need Most “AI agents” in production are deterministic workflows wearing an LLM costume. 11 min read
How to Build Synthetic Data for RAG Evaluation A leakage-first, adversarial-negative pipeline to generate, label, and validate a synthetic RAG evaluation set without building a… 8 min read
How to Use MLflow LLM Evaluation Tracing (Spans → Gates) Turn MLflow traces into living eval datasets, rubric scorecards, and CI/CD regression gates for tool-calling agents. 8 min read