Redis research warns that fine-tuning RAG embedding models for “compositional sensitivity” can quietly harm general retrieval, dropping accuracy up to 40% on production mid-size models. The issue: structural meaning shifts like negation and role reversals can end up near-identical in embedding space, while common fine-tuning metrics miss it. Agentic pipelines are especially vulnerable.
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