Your LLM Is Only as Good as What It Retrieves | Weaviate

This post details the critical role of retrieval quality in Retrieval-Augmented Generation (RAG) systems and its impact on LLM output. It identifies five specific retrieval failure modes: retrieval drift, context truncation, stale index poisoning, low-relevance top-k retrieval, and inter-agent miscommunication. It proposes four key areas for improving retrieval quality: embedding model selection, chunking architecture, retrieval strategy (including hybrid search, cross-encoder re-ranking, and relevance thresholding), and index maintenance and freshness. Finally, it outlines a practical measurement framework for evaluating retrieval quality using metrics like context precision and context recall.