Your Data Pipeline Passes Every Check While Your LLM Quietly Degrades
TL;DR — Schema validation, null checks, and row-count monitors catch structural failures in AI data pipelines, but they're blind to semantic drift — the slow, silent shift in meaning caused by changes to cleaning, normalization, and dedup logic. Models and RAG systems degrade from this constantly w…
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- 2026-10-10 13:16 · DEV Community — Machine Learning
Your Data Pipeline Passes Every Check While Your LLM Quietly Degrades