> ## Documentation Index
> Fetch the complete documentation index at: https://docs.overmindlab.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Decision models and Jev

> Distinguish typed probability prediction, task training and expectation scoring.

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](/tutorials/index.md) results.

Explain when a decision model is useful for fraud detection and when a chat model would be a better fit. Use a transaction example to compare their inputs, outputs and evaluation needs.

## 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.

## 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.

## 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](/how-to/compare-decisions.md) for your probability-model benchmark and [Evaluation reference](/reference/evaluation.md) for the platform's matrix scoring contract.


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