Jev's three-model stack: frontier LLM proposes, Jev measures, CatBoost learns
TypeSafe's wine experiment shows a discover-measure-learn-enforce architecture where Jev turns concepts into probabilities and uncertainty, CatBoost makes the prediction, and no single model does everything.
THE BEST JEV SYSTEM MAY USE THREE MODELS AND JEV IS NOT THE SMARTEST ONE TypeSafe's wine experiment shows a stack that looks nothing like the usual “one giant agent” architecture First, a frontier LLM proposes concepts that may matter inside messy text Second, Jev measures those concepts across every row as probabilities, choices, expected scores and uncertainty Third, CatBoost learns which features actually predict the label The final policy comes from your data. Jev does not need to write the answer. The frontier model does not need to reread every record. The classical model does not need to understand language Each component does the job it is structurally good at That matters because most “AI architecture” today means forcing one model to perceive, reason, decide, explain and act inside one prompt This stack breaks the job apart: discover measure learn enforce The future may not belong to the model with the biggest context window It may belong to the system that gives every model less work
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