# Define the metrics
hallucination_metric = Hallucination()


evaluation = evaluate(
    experiment_name="Existing experiment example",
    dataset=dataset,
    task=evaluation_task,
    scoring_metrics=[hallucination_metric],
    experiment_config={
        "model": MODEL
    }
)

experiment_name = evaluation.experiment_name
print(f"Experiment name: {experiment_name}")
```

<Tip>
  Learn more about the 

  `evaluate`

   function in our 

  [LLM evaluation guide](/evaluation/evaluate_your_llm)

  .
</Tip>

### Update the experiment

Once the first experiment is created, you realise that you also want to compute a moderation score for each example. You could re-run the experiment with new scoring metrics but this means re-running the output. Instead, you can simply update the experiment with the new scoring metrics:

```python
from opik.evaluation import evaluate_experiment
from opik.evaluation.metrics import Moderation

moderation_metric = Moderation()

evaluate_experiment(experiment_name="already_existing_experiment", scoring_metrics=[moderation_metric])
```