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A decision model receives state, a question and a set of options. Its output is a probability for each option in the supplied order. This differs from generating answer text and then extracting a label. Ask this in the project conversation after creating the relevant tutorial or workflow results.

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Preserve the supervision you have

A probability distribution can retain disagreement or uncertainty that an argmax label removes. A hard categorical observation can map to a one-hot target when the label and option mapping are known. A mean-only ordinal observation does not imply a unique distribution of votes. For ordinal means, retain the declared option scale and target mean. For distributions, retain every option probability, ties and example weights. The coding agent must establish their meaning from source evidence rather than from the numeric shape alone.

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Training and inference

The qualified Clef training path uses a separately pinned decision runtime, a backbone adapter and full-precision decision head. Verification checks saved components and probability behaviour. It establishes serialization/execution properties, not task quality. A decision deployment accepts typed questions and returns probabilities. Chat messages, output-token budgets and chat aliases are separate contracts. Model catalogue visibility also differs from launch qualification; D3 and D1 paths remain disabled until qualified.

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Jev as an evaluation engine

The platform uses Jev through its dedicated decision transport to score llm expectation rows. This scoring role is distinct from training a model on your dataset. A generative model supplies explanations for low scores; it does not silently replace failed Jev decisions. Use Compare decision models for your probability-model benchmark and Evaluation reference for the platform’s matrix scoring contract.