Run evaluations in the UI

Create background tasks to run evaluations without code

This guide will show you how to setup an online evaluation in the Arize UI, which runs on your data automatically as your LLM application is used. You can setup LLM as a judge evaluators or code evaluators, which run in a python container against your data.

Follow this guide to setup code evaluations. The setup is quite similar to LLM evaluations.

Incoming traces are populated with evaluation labels every 5min using an Online Task.

Step 1: Create a new task

Navigate to the tasks page and click "new task".

Select New Task on the Tasks page.

Step 2: Select your data

Choose which traces you want to evaluate, and how often you want to run it. You can run it once to backfill eval labels on historical data, or you can run it continuously against new data within 5 minutes of its arrival in the platform.

You can apply specific filters to tag the right data, and also define a sampling rate. For instance, setting it to 50% means the task will run on half of the incoming traces.

Fill out the New Task column on the left.

Step 3: Choose your LLM

Choose the LLM provider, model, and other parameters for your LLM evaluation. Configure models on the integrations page to make them available.

Define the LLM Parameters.

Step 4: Define your evaluator

Select one of our pre-built evaluators, or use copilot to write one on your behalf! You can also write your own evaluation template or write python code to evaluate your LLM outputs. You can use our pre-tested, off-the-shelf evaluators as well.

If you are writing a custom eval template, use bracket notation to reference specific dataframe fields, such as {attributes.llm.input_messages.0.message.content}, which follow the openinference semantic conventions. The task will use this template to run the llm_classify function from Phoenix under the hood.

You can edit the output rails to constrain the generated eval labels. If there are two options, the first value will be mapped to 1, and the second will be mapped to 0, which allows for aggregate scoring.

Define the LLM Evaluator.

Step 5. View Evaluation Labels on LLM Tracing Page

Once your task is successfully created, a green pop-up notification will appear! Navigate to the tracing page to view the evaluation labels generated by your newly created task.

New task appears on the Tasks page. Green pop up provides a link to the LLM Tracing page, where you can see your evaluation labels.

View labels in the Evaluations column of the LLM Tracing page associated with the project selected in the task.

Use Copilot to Write Your Task

Need help creating an eval? Use ✨Copilot to easily generate your evaluation template. Simply click the Copilot button within the task modal, and describe the evaluation you want to create or its goals. Copilot will handle the rest—naming the evaluation, generating a prompt with the necessary variables, and including the appropriate guardrails.