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JevMade field notes / Conceptual walkthrough

LLMs generate answers, Jev makes decisions

Paarangat uses account-risk decisions to show how Jev fits between messy human context and deterministic code, while generative models remain available for deeper reasoning or writing.

Original by PaarangatAgent workflowsX postOriginal published Source reviewed

Before you dive in

What you’ll find in the original

  1. Define permitted outcomes such as risk level and manual review before inference; Jev returns probabilities for those decisions.
  2. Constrained output prevents invented labels but does not guarantee correctness—a high-probability answer can still be wrong.
  3. Use Jev as the routing layer, generative models for deeper reasoning or generation, and ordinary code for deterministic execution.
Worth knowing

This is an architectural explainer, not an evaluation; examples and probabilities are illustrative, and the author calls the technology early.