Arize-Evaluator

# Arize Evaluator Skill

> **`SPACE`** — All `--space` flags and the `ARIZE_SPACE` env var accept a space **name** (e.g., `my-workspace`) or a base64 space **ID** (e.g., `U3BhY2U6...`). Find yours with `ax spaces list`.

This skill covers designing, creating, and running **LLM-as-judge evaluators** on Arize. An evaluator defines the judge; a **task** is how you run it against real data.

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Prerequisites

Proceed directly with the task — run the `ax` command you need. Do NOT check versions, env vars, or profiles upfront.

If an `ax` command fails, troubleshoot based on the error:

- `command not found` or version error → see references/ax-setup.md

- `401 Unauthorized` / missing API key → run `ax profiles show` to inspect the current profile. If the profile is missing or the API key is wrong, follow references/ax-profiles.md to create/update it. If the user doesn't have their key, direct them to https://app.arize.com/admin > API Keys

- Space unknown → run `ax spaces list` to pick by name, or ask the user

- LLM provider call fails (missing OPENAI_API_KEY / ANTHROPIC_API_KEY) → run `ax ai-integrations list --space SPACE` to check for platform-managed credentials. If none exist, ask the user to provide the key or create an integration via the **arize-ai-provider-integration** skill

- **Security:** Never read `.env` files or search the filesystem for credentials. Use `ax profiles` for Arize credentials and `ax ai-integrations` for LLM provider keys. If credentials are not available through these channels, ask the user.

- **CRITICAL — Never fabricate evaluation results:** If an evaluation task fails, is cancelled, or produces no scores, report the failure clearly and explain what went wrong. Do NOT perform a "manual evaluation," invent quality scores, estimate percentages, or present any agent-generated analysis as if it came from the Arize evaluation system. Instead suggest: (1) fix the identified issue and retry, (2) try running from the Arize UI, (3) verify integration credentials with `ax ai-integrations list`, (4) contact support at https://arize.com/support

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Concepts

What is an Evaluator?

An **evaluator** is an LLM-as-judge definition. It contains:

| Field | Description |

|-------|-------------|

| **Template** | The judge prompt. Uses `{variable}` placeholders (e.g. `{input}`, `{output}`, `{context}`) that get filled in at run time via a task's column mappings. |

| **Classification choices** | The set of allowed output labels (e.g. `factual` / `hallucinated`). Binary is the default and most common. Each choice can optionally carry a numeric score. |

| **AI Integration** | Stored LLM provider credentials (OpenAI, Anthropic, Bedrock, etc.) the evaluator uses to call the judge model. |

| **Model** | The specific judge model (e.g. `gpt-4o`, `claude-sonnet-4-5`). |

| **Invocation params** | Optional JSON of model settings like `{"temperature": 0}`. Low temperature is recommended for reproducibility. |

| **Optimization direction** | Whether higher scores are better (`maximize`) or worse (`minimize`). Sets how the UI renders trends. |

| **Data granularity** | Whether the evaluator runs at the **span**, **trace**, or **session** level. Most evaluators run at the span level. |

Evaluators are **versioned** — every prompt or model change creates a new immutable version. The most recent version is active.

What is a Task?

A **task** is how you run one or more evaluators against real data. Tasks are attached to a **project** (live traces/spans) or a **dataset** (experiment runs). A task contains:

| Field | Description |

|-------|-------------|

| **Evaluators** | List of evaluators to run. You can run multiple in one task. |

| **Column mappings** | Maps each evaluator's template variables to actual field paths on spans or experiment runs (e.g. `"input" → "attributes.input.value"`). This is what makes evaluators portable across projects and experiments. |

| **Query filter** | SQL-style expression to select which spa