Your RAG Gets Confidently Wrong as Memory Grows – I Built the Memory Layer That Stops It

Retrieval-Augmented Generation (RAG) systems can fail silently as their memory or knowledge base grows with irrelevant information. A controlled experiment shows that as memory size increases, accuracy drops significantly while the system's reported confidence paradoxically rises. This dangerous failure mode occurs because irrelevant but contextually similar "plausible noise" achieves high similarity scores, crowding out genuinely correct documents. Since confidence is often calculated from these misleading similarity scores, it becomes an unreliable signal for system performance. The proposed solution is to implement a managed memory layer with mechanisms like topic routing, deduplication, and relevance eviction to ensure a smaller, well-chosen set of entries outperforms a large, uncurated one.