Classifying and Addressing the Diversity of Errors in Retrieval-Augmented Generation Systems

• URL: http://arxiv.org/abs/2510.13975v1
• Date: Wed, 15 Oct 2025 18:02:30 GMT
• Title: Classifying and Addressing the Diversity of Errors in Retrieval-Augmented Generation Systems
• Authors: Kin Kwan Leung, Mouloud Belbahri, Yi Sui, Alex Labach, Xueying Zhang, Stephen Rose, Jesse C. Cresswell,
• Abstract summary: Retrieval-augmented generation (RAG) is a prevalent approach for building question-answering systems.<n>Due to the complexity of real-world RAG systems, there are many potential causes for erroneous outputs.<n>We present a new taxonomy of the error types that can occur in realistic RAG systems, examples of each, and practical advice for addressing them.
• Score: 10.899541303791928
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Retrieval-augmented generation (RAG) is a prevalent approach for building LLM-based question-answering systems that can take advantage of external knowledge databases. Due to the complexity of real-world RAG systems, there are many potential causes for erroneous outputs. Understanding the range of errors that can occur in practice is crucial for robust deployment. We present a new taxonomy of the error types that can occur in realistic RAG systems, examples of each, and practical advice for addressing them. Additionally, we curate a dataset of erroneous RAG responses annotated by error types. We then propose an auto-evaluation method aligned with our taxonomy that can be used in practice to track and address errors during development. Code and data are available at https://github.com/layer6ai-labs/rag-error-classification.

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