RAG Systems: Retrieval Quality Drives Hallucinations

AI Impact Summary

Poor retrieval quality is the single most reliable predictor of degraded output in RAG systems, as highlighted by the source material. The core issue isn't model size or prompt design, but rather the accuracy and relevance of the information being retrieved from the vector database. Failure modes like retrieval drift, context truncation, and stale index poisoning consistently lead to hallucinations, even in large language models, because the model has no mechanism to detect or correct inaccurate context.