@inproceedings{garani-2026-systematic,
    title = "A Systematic Taxonomy of Failure Modes in Retrieval-Augmented Generation Systems",
    author = "Garani, Anupama",
    editor = "Chang, Kai-Wei  and
      Mehrabi, Ninareh  and
      Krishna, Satyapriya  and
      Das, Anubrata  and
      Dhamala, Jwala  and
      Cao, Yang Trista  and
      Kumarage, Tharindu  and
      Ramakrishna, Anil  and
      Christodoulopoulos, Christos  and
      Wan, Yixin  and
      Galystan, Aram  and
      Kumar, Anoop  and
      Gupta, Rahul",
    booktitle = "Proceedings of the 6th Workshop on Trustworthy {NLP} ({T}rust{NLP} 2026)",
    month = jul,
    year = "2026",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.trustnlp-main.27/",
    pages = "413--424",
    ISBN = "979-8-89176-418-7",
    abstract = "Retrieval-Augmented Generation (RAG) systems fail in diverse, poorly characterized ways that single-stage evaluation metrics cannot detect. We present a systematic taxonomy of 33 failure modes across 7 pipeline stages {---} ingestion, representation, retrieval, generation, evaluation, deployment, and agentic orchestration {---} constructed through a structured literature review of 48 sources spanning peer-reviewed publications and high-impact preprints. For each mode, we provide a formal definition, observable manifestation, and three-level evidence grading (Strong/Moderate/Limited). Our analysis reveals a critical asymmetry in research attention: retrieval and generation failures are comparatively well-studied, while representation, evaluation, and agentic orchestration failures remain under-investigated despite frequent occurrence in production. We identify 12 failure modes with no dedicated peer-reviewed empirical evidence {---} all 8 agentic modes among them {---} constituting an evidence desert in the fastest-growing RAG deployment paradigm. Compared to prior work enumerating 7 failure points (Barnett et al., 2024) or 16 error types within partial pipeline runs (Cresswell et al., 2025), our taxonomy uniquely spans the full pipeline, including agentic orchestration with explicit evidence-level grading."
}