To monitor hallucination rates in RAG-based systems, developers can use five methods: 1) Manual Review (human evaluation of generated responses for accuracy), 2) Automated Evaluation (using tools to compare responses against ground truth or trusted sources, such as CRAG), 3) Synthetic Adversarial Queries (creating challenging test cases to provoke hallucinations), 4) User Feedback (collecting surveys or Net Promoter Scores), and 5) Precision@k/Recall@k (measuring the quality of retrieved documents).

Authors

• Person: Sumit Umbardand Organization: LinkedIn
  RAG Hallucinations: Retrieval Success ≠ Generation Accuracy

Sources

• RAG Hallucinations: Retrieval Success ≠ Generation Accuracy www.linkedin.com Sumit Umbardand · LinkedIn via serper

Referenced by nodes (1)

• Retrieval-Augmented Generation (RAG) concept