> For the complete documentation index, see [llms.txt](https://docs.modelmetry.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.modelmetry.com/platform/evaluators.md). # Evaluators In short, evaluators are small software we run that you can configure which evaluate specific aspects of a payload. A payload is your user's input or your LLM's output. Some evaluators focus on readability, others on JSON structure if you expect JSON from your model, or even safety by checking for prompt injection and jailbreaks. To get started, you first pick the evaluator of your choice and create an [instance](/platform/evaluators/instances.md). An instance is essentially a configured evaluator. Nearly all evaluators are configurable so you can run multiple instances of the same evaluator with slightly different settings. {% hint style="success" %} [**View a list of all Modelmetry evaluators in the documentation.**](/evaluators/all.md) {% endhint %} In your team, you can list all of Modelmetry evaluators by going to `Evaluators` in the sidebar. The list is searchable and filterable. ### View details of an evaluator 1. Go to `Evaluators` 2. Locate the evaluator you want to see the details of and click the `Details` button The details page of an evaluator shows you some general information as well as the findings generated by the evaluator, as well as its configuration settings. In the `Instances` section, you can see a list of your current instances for that evaluator.

Example of the details screen for `azure.prompt-shields.v1`

### Test an evaluator 1. Go to `Evaluators` 2. Locate the evaluator you want to see the details of and click the `Test` button 3. Fill in the configuration, request, and grading data 1. If the evaluator uses an LLM, attach the needed secret to it