Seven Failure Points When Engineering a Retrieval Augmented Generation System

This article identifies key challenges in implementing RAG systems, which combine document retrieval with language models like ChatGPT. It highlights seven potential failure points, including missing content, missed top-ranked documents, context issues, extraction errors, format problems, incorrect specificity, and incomplete answers. The paper emphasises the need for thorough testing and refinement of RAG systems, citing case studies that illustrate how larger contexts and metadata improve performance and the parity of open-source embedding models with closed-source ones in certain contexts.

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