Context rot, a phenomenon affecting large language models (LLMs), leads to performance degradation as these models process longer input contexts. A 2023 Stanford study highlights that accuracy can drop significantly due to a "lost-in-the-middle" problem, where relevant information buried in the middle of long contexts receives less attention. This issue is compounded by positional encoding limitations and task-dependent attention mechanism degradation. Context rot results in reduced response quality, increased computational costs, and necessitates complex architectural solutions. Detecting context rot requires a multi-layered monitoring approach, and addressing it involves using external memory architectures that maintain fixed context windows while dynamically retrieving relevant information, thereby reducing redundant processing. Solutions like Redis employ semantic caching and dynamic retrieval through vector databases to combat context rot effectively, offering an integrated approach to enhance LLM performance.