Retrieval-augmented generation (RAG) systems can fail due to issues in the document processing pipeline, rather than solely model-layer problems. Three common failure modes include scan rot from poor optical character recognition (OCR) on image-based documents, duplicate chunks overwhelming vector stores with redundant information, and leaky indexes that expose sensitive data. Addressing these issues requires robust data engineering to recover document structure and implement deduplication strategies before indexing and retrieval. AI
IMPACT Improves the reliability and accuracy of RAG systems by addressing upstream data processing issues.
RANK_REASON Article details specific failure modes and fixes for a particular AI application (RAG).
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