Memory Grows, Accuracy Drops: The Unseen Consequences of RAG Systems The Silent Failure of RAG Systems: How Memory Growth Affects Accuracy RAG (Reinforcement Learning, Attention, and Generation) systems have revolutionized the field of natural language processing, enabling machines to generate human-like text. However, a recent study has uncovered a surprising phenomenon: as memory grows in RAG systems, accuracy quietly drops while confidence rises. This silent failure can have devastating consequences, as most monitoring systems never detect it. In this post, we'll delve into the reproducible experiment that demonstrates this issue and explore a simple memory architecture fix to restore reliability. The Problem: Memory Growth and Accuracy Drop RAG systems rely on memory to store and retrieve information. As the system processes more data, its memory grows, allowing it to learn from a broader range of experiences.…