> ## Documentation Index
> Fetch the complete documentation index at: https://arizeai-433a7140.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Decision Models as a Judge

> A decision model is a judge that answers a typed classification question directly, returning a label with per-label probabilities instead of free-form text.

> [!NOTE]
>
> Decision models are an alternative to language-model judges for classification-style evaluations. Both are used the same way in the Phoenix SDK: you pass either one as an evaluator's judge. See [LLM as a Judge](https://arizeai-433a7140.mintlify.site/docs/phoenix/evaluation/concepts-evals/llm-as-a-judge.md) for the language-model approach.

A language-model judge reads a prompt and generates text, then Phoenix parses a label and an explanation from that text. A **decision model** works differently: it is constrained to answer one typed question; here, which label applies; and returns the chosen label together with a probability for each option (and a confidence when the provider reports one). Because it does not generate free-form text, it returns **no explanation**.

This makes a decision model a natural fit for classification, where the task is exactly to pick one label from a fixed set. You trade the written rationale of an LLM judge for a direct, structured choice with per-label probabilities.

## When to Use a Decision Model

Use a decision model when:

* The evaluation is a classification with a fixed set of labels (binary or multi-class).
* You want calibrated per-label probabilities rather than a single label.
* You do not need a written explanation for each judgement.

Use a [language-model judge](https://arizeai-433a7140.mintlify.site/docs/phoenix/evaluation/concepts-evals/llm-as-a-judge.md) instead when you need a human-readable rationale, or when the task is not a simple classification.

## How It Works

**1. Identify Evaluation Criteria**

Determine what you want to evaluate, such as faithfulness, toxicity, or relevance. See the [pre-built evaluators](https://arizeai-433a7140.mintlify.site/docs/phoenix/evaluation/pre-built-metrics.md) for examples.

**2. Define the Labels**

A decision model answers a choice question, so define the set of labels (the `choices`) it must pick from.

**3. Select a Decision Model**

Choose a decision model from a supported provider. The decision model is passed to the evaluator in the same place as a language model.

**4. Generate Evaluations and View Results**

Run the evaluator across your data. Each result carries the chosen label, a numeric score, and per-label probabilities (and a confidence when the provider reports one) in its metadata — but no explanation.

## Example

Any classification evaluator, including every [pre-built metric](https://arizeai-433a7140.mintlify.site/docs/phoenix/evaluation/pre-built-metrics.md), accepts a decision model in place of a language model.

> [tab] Python
>
> ```python theme={"theme":{"light":"github-light-default","dark":"github-dark-default"}}
> from phoenix.evals import ClassificationEvaluator, DecisionModel
>
> evaluator = ClassificationEvaluator(
>     name="relevance",
>     prompt_template="Is the reference text relevant to the question?\n{question}\n{reference}",
>     llm=DecisionModel(provider="openai", model="gpt-6-luna"),
>     choices={"relevant": 1.0, "irrelevant": 0.0},
> )
>
> scores = evaluator.evaluate({
>     "question": "Where is the Eiffel Tower?",
>     "reference": "The Eiffel Tower is in Paris, France.",
> })
> print(scores[0])
> # Score(label='relevant', score=1.0, metadata={'probabilities': {...}}, ...)  # no explanation
> ```

> [tab] TypeScript
>
> ```typescript theme={"theme":{"light":"github-light-default","dark":"github-dark-default"}}
> import { createClassificationEvaluator } from "@arizeai/phoenix-evals";
> import { openai } from "@ai-sdk/openai";
>
> const evaluator = createClassificationEvaluator({
>   name: "relevance",
>   model: openai.decisionModel("gpt-6-luna"),
>   promptTemplate:
>     "Is the reference text relevant to the question?\n{{question}}\n{{reference}}",
>   choices: { relevant: 1, irrelevant: 0 },
> });
>
> const result = await evaluator.evaluate({
>   question: "Where is the Eiffel Tower?",
>   reference: "The Eiffel Tower is in Paris, France.",
> });
> ```

## Using Decision Models in Phoenix

> [card] [Custom Evaluators](https://arizeai-433a7140.mintlify.site/docs/phoenix/evaluation/how-to-evals/custom-llm-evaluators.md)
>
> Build classification evaluators in Python or TypeScript, backed by a language model or a decision model.

> [card] [Configuring the Decision Model](https://arizeai-433a7140.mintlify.site/docs/phoenix/evaluation/how-to-evals/configuring-the-decision-model.md)
>
> Decision providers, their adapters, and required parameters.