Mindful-RAG: A Study of Points of Failure in Retrieval Augmented Generation

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Abstract

Large Language Models (LLMs) excel at generating coherent text but often struggle with knowledge-intensive queries, particularly in domain-specific and factual question-answering tasks. Retrieval-augmented generation (RAG) systems have emerged as a promising solution by integrating external knowledge sources, such as structured knowledge graphs (KGs). While KG-based RAG approaches have demonstrated value, current state-of-the-art solutions frequently fall short, failing to deliver accurate and reliable answers even when the necessary factual knowledge is available. In this paper, we present a critical analysis of failure points in existing KG-based RAG methods, identifying eight key areas of concern, including misinterpretation of question context, incorrect relation mapping, and ineffective ambiguity resolution. We argue that these failures primarily stem from design limitations in current KG-RAG systems, such as inadequate attention to discerning user intent and insufficient alignment of retrieved knowledge with the contextual demands of the query. Based on this analysis, we propose a new approach for KG-RAG systems, termed Mindful-RAG, which re-engineers the retrieval process to be more intent-driven and contextually aware. By enhancing reasoning capabilities, improving constraint identification, and addressing the structural limitations of knowledge graphs, we aim to improve the reliability and effectiveness of KG-RAG systems. To validate this approach, we developed a proof-of-concept by integrating the principles of Mindful-RAG into an existing KG-RAG system. The Mindful-RAG approach seeks to deliver more robust, accurate, and contextually aligned AI-driven knowledge retrieval systems, with potential applications in critical domains such as healthcare, legal, research, and scientific discovery, where precision and reliability are paramount.