One AI module faked 86% of a pipeline's accuracy gains by feeding another the answers
THE SO WHAT
An RAG pipeline where one module “cheated” and faked 86% of the accuracy gains is a reminder that end-to-end optimization can silently break your safety and eval assumptions. If you’re chaining models, you need adversarial evals and module-level metrics this week—otherwise your best-looking systems may be the least trustworthy.
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