> ## Documentation index
> Fetch the complete documentation index at: https://arize.com/llms.txt
> Ask it with GET https://arize.com/ask?q=<keywords or question>. Short keywords rank best.
> Use these to discover all available pages before exploring further.

# Classification

> Classification helpers in @arizeai/phoenix-evals

Use the classification helpers when you want an LLM to choose from a fixed set of labels and return a structured explanation.

## Create A Classifier Function

```ts
import { openai } from "@ai-sdk/openai";
import { createClassifierFn } from "@arizeai/phoenix-evals";

const classify = createClassifierFn({
  model: openai("gpt-4o-mini"),
  choices: { relevant: 1, irrelevant: 0 },
  promptTemplate:
    "Question: {{input}}\nContext: {{context}}\nAnswer: {{output}}\nLabel as relevant or irrelevant.",
});

const result = await classify({
  input: "What is Phoenix?",
  context: "Phoenix is an AI observability platform.",
  output: "Phoenix helps teams inspect traces and experiments.",
});
```

## Decision Models

Any classification evaluator, including every built-in, can run on an AI SDK decision model such as OpenAI's `gpt-6-luna` ([Decisions API](https://developers.openai.com/api/docs/guides/decisions)) or TypeSafe's Jev. Decision models pick one of the labels directly and return a probability for each, instead of generating text. They are much faster and cheaper than a chat model judge. With OpenAI, use `@ai-sdk/openai` 4.0.89 or later.

```ts
import { openai } from "@ai-sdk/openai";
import { createCorrectnessEvaluator } from "@arizeai/phoenix-evals";

const correctness = createCorrectnessEvaluator({
  model: openai.decisionModel("gpt-6-luna"),
});

const result = await correctness.evaluate({
  input: "What is the capital of Australia?",
  output: "Sydney.",
});
// {
//   label: "incorrect",
//   score: 0,
//   metadata: {
//     probabilities: { correct: 0.02, incorrect: 0.98 },
//     confidence: 0.96,
//     modelId: "gpt-6-luna",
//   },
// }
```

How it differs from a language model:

- **No explanation.** Decision models don't generate text, so `explanation` is always undefined.
- **Probabilities in metadata.** `metadata.probabilities` maps each label to its probability. `metadata.confidence` is included when the provider reports one (OpenAI and TypeSafe do).
- **Prompt shape.** The template is rendered the same way as for a language model, then sent as one plain-text state. Message roles are dropped and only text content is sent.
- **Choices.** Each provider sets its own limits on how many choices a question can have. A classification outside them fails with the provider's error.
- **Refusals.** A decision model may refuse to classify, for example when asked to infer sensitive attributes rejected by the chosen model. On a refusal, the evaluator throws the AI SDK's `Experimental_DecisionRefusalError`. In `runExperiment`, it's recorded as that evaluation's error.
- **Plain model IDs.** A string model ID is always treated as a language model. Use the provider's `decisionModel()` to get a decision model.

<Warning>
Decision models can't work through a problem step by step. They do well on judgments you can read off the text, such as relevance, faithfulness, tool selection, and toxicity. They're unreliable for checking answers that need a calculation, such as unit conversions or arithmetic, and their probabilities can be confidently wrong on those. Use a language model for those evaluations.
</Warning>

## Lower-Level API

Use `generateClassification` directly when you already have a rendered prompt and only need structured label generation. It also accepts a decision model, in which case the rendered prompt is sent as one plain-text state.

<section className="hidden" data-agent-context="source-map" aria-label="Source map">
  <h2>Source Map</h2>
  <ul>
    <li><code>src/llm/createClassifierFn.ts</code></li>
    <li><code>src/llm/createClassificationEvaluator.ts</code></li>
    <li><code>src/llm/generateClassification.ts</code></li>
    <li><code>src/decision/decideClassification.ts</code></li>
    <li><code>src/decision/decisionPrompt.ts</code></li>
    <li><code>src/types/evals.ts</code></li>
  </ul>
</section>