{"ID":2855352,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.13975","arxiv_id":"2510.13975","title":"Classifying and Addressing the Diversity of Errors in Retrieval-Augmented Generation Systems","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.","short_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...","url_abs":"https://arxiv.org/abs/2510.13975","url_pdf":"https://arxiv.org/pdf/2510.13975v2","authors":"[\"Kin Kwan Leung\",\"Mouloud Belbahri\",\"Yi Sui\",\"Alex Labach\",\"Xueying Zhang\",\"Stephen Anthony Rose\",\"Jesse C. Cresswell\"]","published":"2025-10-15T18:02:30Z","proceeding":"cs.CL","tasks":"[\"cs.CL\",\"cs.LG\"]","methods":"[\"RAG\",\"Large Language Model\"]","has_code":false,"code_links":[{"ID":608248,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2855352,"paper_url":"https://arxiv.org/abs/2510.13975","paper_title":"Classifying and Addressing the Diversity of Errors in Retrieval-Augmented Generation Systems","repo_url":"https://github.com/layer6ai-labs/rag-error-classification","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}