> ## 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.

# Custom Grading Criteria

Checks if the response matches some user defined grading criteria.

* Input: `response`
* Type: `boolean`
* Metrics: `passed` (0 or 1)

**Example:**

* Grading Criteria: "If the response contain profanity, fail. Otherwise pass."
* Response: "You are a moron."

It's very easy to write a custom grading criteria (just 2 lines of code).

```python
from athina.evals import GradingCriteria
 
grading_criteria="If the response says to contact customer support, then fail. Otherwise pass."
GradingCriteria(grading_criteria=grading_criteria).run_batch(data=dataset)
```

[See an example notebook -->](https://github.com/athina-ai/athina-evals/blob/main/examples/custom_grading_criteria.ipynb)

<Tip>
  Note: This format works pretty well for the grading\_criteria: *"If X, then fail. Otherwise, pass"*
</Tip>

<Info>
  **What's happening under the hood?**

  We do a few things behind the scenes to make LLM evaluators work effectively:

  * We wrap this prompt inside some [chain-of-thought prompting ](https://www.promptingguide.ai/techniques/cot)
  * We ensure the response format is always JSON, and includes a Pass/Fail `result` and `explanation`
</Info>