Your LLM Is Only as Good as What It Retrieves

Research into hallucination detection in multi-agent LLM systems highlights that retrieval quality, rather than model size or configuration, significantly impacts the accuracy and reliability of outputs. Retrieval-Augmented Generation (RAG) systems use an external knowledge store to provide context for language models, but when retrieval fails, models can produce confident yet incorrect outputs. Key failure modes include retrieval drift, context truncation, stale index poisoning, low-relevance top-k retrieval, and inter-agent miscommunication. Addressing these issues involves improving embedding models, chunking architecture, retrieval strategies, and index maintenance while employing metrics like context precision, recall, and faithfulness to evaluate retrieval quality. The research emphasizes that retrieval quality is the most critical factor for ensuring factual accuracy and recommends focusing on retrieval improvements over model scaling, particularly in complex multi-agent systems.