> For the complete documentation index, see [llms.txt](https://docs.dbnl.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.dbnl.com/v0.20.x/using-distributional/python-sdk/eval-module.md).

# Eval Module

Many generative AI applications focus on text generation. It can be challenging to create metrics for  insights into expected performance when dealing with unstructured text.<br>

`dbnl.eval` is a special module designed for evaluating unstructured text. This module currently includes:

* Adaptive metric sets for generic text and RAG applications
* 12+ simple statistical local library powered text metrics
* 15+ LLM-as-judge and embedding powered text metrics
* Support for user-defined custom LLM-as-judge metrics&#x20;
* LLM-as-judge metrics compatible with OpenAI, Azure OpenAI

Building dbnl tests on these evaluation metrics can then drive rich insights into an AI application's stability and performance.