RAG Data Quality: Old Problems in a New Bottle

Even with meticulous cleaning, residual noise, redundant legal clauses, and approximate duplicates can degrade retrieval and generation in RAG systems, while privacy risks from embedding inversion and the need for continuous, metric‑driven governance make data quality the ultimate ceiling for performance.

Embedding InversionLLM RetrievalPrivacy

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RAG Data Quality: Old Problems in a New Bottle