RAG Tuning May Cut Retrieval Accuracy by 40%

New research from Redis indicates that fine-tuning Retrieval-Augmented Generation (RAG) embedding models for precision can inadvertently reduce retrieval accuracy by up to 40%. This occurs because training for compositional sensitivity, which distinguishes subtle meaning differences, degrades the model's ability to generalize across broad topics. Such degradation poses significant risks for agentic AI pipelines, where retrieval errors can cascade into incorrect actions. The findings challenge the assumption that high semantic similarity guarantees correct intent, highlighting a trade-off between precision tuning and broad retrieval performance that current solutions often fail to address.