# we compute the final Coverage score
score_coverage = get_final_score(assessed_claims_coverage)
print(f'Coverage: {score_coverage}')
```

```txt title="Output"
Coverage: 0.33
```

The Coverage score is telling us that 1/3 of the information in the gold answer is present in the generated answer. This is a useful information, that, similarly to what said above regarding Correctness, can raise further questions, such as: is it acceptable to have diverging information in the generated answer? Is any crucial piece of information missing in the generated answer?

The answer to these questions is use case-specific, and has to be made by the end user: The claim-based approach implemented here supports the user by providing a clear and detailed view on what the model is assessing and how.

## Final Comments \[#final-comments]

RAG evaluation is a hard task, especially the evaluation of the generated response. In this notebook we offer a clear, robust and replicable approach to evaluation, on which you can build on to build your evaluation pipeline.