Your LLM Is Only as Good as What It Retrieves

In my research on hallucination detection in multi-agent LLM systems, the most consistent findings have not been about model size, prompt design, or inference temperature. It has been about retrieval. Poor retrieval quality is the single most reliable predictor of degraded output across every pipeline configuration I have studied.The evidence from our experimental pipelines is unambiguous: when retrieval breaks down, the language model does not compensate. It extrapolates.