RAG systems often produce incorrect statements, which are frequently mislabeled as hallucinations. Distinguishing between retrieval failures and generation errors is essential for effective system optimization.
Research from Vectara indicates that state-of-the-art hallucination detectors achieve only about 50% accuracy on complex datasets. Standard NLI models often fail to determine if a context actually influenced the generation process.
A systematic diagnostic approach involves analyzing top-k retrieval results, performing ablation studies by removing context chunks, and checking for reliance on parametric memory. These steps help identify whether the issue lies in chunking, retrieval, or prompt engineering.
Implementing structural changes, such as semantic chunking and mandatory attribution checks, can significantly improve performance. In one case, these adjustments reduced the share of unsubstantiated claims from 34% to 9%.
