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Copyright © 2023 Arize AI, Inc
Last updated
You can create and run an experiment with the following steps:
Install Arize Client
Setup client
Grab your Space ID, Developer Key, and API key from the UI
Datasets are useful groupings of data points you want to use for test cases. You can create datasets through code, using LLMs to auto-generate them, or by importing spans using the Arize UI. Below is sample code to create a dataset from a JSON dataframe.
If you have added tracing to your application, you can create datasets by adding spans from your application with Arize. Go to the traces page and filter for the examples you care about.
In the example below, we are filtering for spans with a hallucination label, and adding them to a dataset.
Here is where you define the LLM task you are trying to test for its effectiveness. This could be structured data extraction, SQL generation, chatbot response generation, search, or any task of your choosing.
The input is the dataset_row so you can access the variables in your dataset, and the output is a string.
Navigate to the dataset in the UI and see the experiment output table.
This guide helps you run experiments to test and validate changes in your LLM applications against a curated dataset. Learn more about the .
Use ✨ to curate your dataset with natural language.
An evaluator is any function that (1) takes the task output and (2) returns an assessment. This gives you tremendous flexibility to using a custom template, or use .
We use our OSS package Arize Phoenix to run LLM-based evaluations, using llm_classify
. See here for the .