@arizeai/phoenix-evals/docs/classification.mdx

A library for running evaluations for AI use cases

---
title: "Classification"
description: "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.",
});
```

## Lower-Level API

Use `generateClassification` directly when you already have a rendered prompt and only need structured label generation.

<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/types/evals.ts</code></li>
  </ul>
</section>