> ## 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.

# Write your own LLM Eval class

<Tip>
  Have you seen our [custom evaluation](/evals/custom-evals) wrappers? They are
  a great way to quickly create your own evaluation class.
</Tip>

If you want to write your own evaluation class, you can do so by extending the `BaseEvaluator`
class.

You can extend the `BaseEvaluator` class. See this [example ](https://github.com/athina-ai/athina-evals/blob/main/athina/evals/base_evaluator.py)

You need to implement the following methods:

```python
@property
def _model(self):
    # Which model was used for this evaluation
    pass

@property
def name(self):
    return "UpperCamelCaseName"

@property
def display_name(self):
    return "Evaluation Display Name"

@property
def metric_ids(self) -> List[str]:
    return [MetricType.PASSED.value]

@property
def default_function_arguments(self):
    return {}

@property
def required_args(self):
    # expects an array of strings from ["query", "context", "response", "expected_response", "text"]
    pass

@property
def examples(self):
    pass

def is_failure(self) -> Optional[bool]:
    pass


def _evaluate(self, **kwargs) -> EvalResult:
    pass


```

<Tip>
  **Contribute !**

  If you write an evaluator that could be useful for others, please consider raising a PR at [https://github.com/athina-ai/athina-evals](https://github.com/athina-ai/athina-evals).
</Tip>