> ## Documentation Index
> Fetch the complete documentation index at: https://docs.athina.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Eval Cookbooks

Here are some cookbooks we've prepared to make it easy to set up and run evals using Athina.

1. [Run a preset eval ](https://github.com/athina-ai/athina-evals/blob/main/examples/run_eval.ipynb): This cookbook shows you how to run a single eval on your dataset
2. [Run an eval suite ](https://github.com/athina-ai/athina-evals/blob/main/examples/run_eval_suite.ipynb): This cookbook shows you how to run a suite of evals
3. [Run an experiment ](https://github.com/athina-ai/athina-evals/blob/main/examples/run_experiment.ipynb) This cookbook shows how to run an eval using Athina, and also log the experiment configuration.

*This is very similar to #1, but you are also describing an `AthinaExperiment` object, so the experiments will be logged to your develop dashboard, along with the metadata and experiment parameters (like prompt).*

4. [Run an eval with a custom grading criteria ](https://github.com/athina-ai/athina-evals/blob/main/examples/run_experiment.ipynb)

A custom grading criteria is the easiest way to create your own eval.

These evals take the format: *"If X, then fail. Otherwise, pass"*

This gets wrapped inside our CoT prompt, and enforces a JSON output of pass / fail along with a reason.

This is best used for very simple conditional evals (like the one below)