# Save to CSV
results_csv = results_df.to_csv("search_results_evaluation.csv", index=False)
```

```

Percentage of questions won by each engine:
Engine A: 80.00% (8/10)
Tie: 10.00% (1/10)
Engine B: 10.00% (1/10)
```

## Conclusion

This tutorial demonstrates how to evaluate retrieval systems using LLMs as judges through Pydantic AI, comparing original Wikipedia search results against those reranked by Cohere's reranking model.

The evaluation framework uses multiple Cohere models as independent judges with majority voting to determine which system provides more relevant results.

Results showed the reranked approach (Engine A) outperformed the original search rankings (Engine B) by winning 80% of queries, demonstrating the effectiveness of neural reranking in improving search relevance.