# Context Rot: Why Claude Code Sessions Decay and How to Govern Them How to manage context degradation in AI models? - **Source:** [Towards Data Science](https://towardsdatascience.com) - **Published:** 2026-07-13 - **Topic:** [Data & AI](https://brifing.si/c/data-ai) - **Original article:** - **On Brifing:** https://brifing.si/s/f07b397e-48fb-4b6d-854a-70950a1eda72 ## What is the Context Window? The context window, a core feature of every AI model, consists of system prompts and interaction history. Before generating new output, the model scans this window. This mechanism allows the model to maintain memory throughout the session. However, the context window is not passive storage; its content shapes the model's output. ## The Concept of Context Rot Context rot refers to the gradual decline in a model's performance due to the content in the context window. This phenomenon negatively impacts output quality. Context rot is categorized into intrinsic and content-based rot. ## Intrinsic Context Rot Intrinsic context rot stems from the performance limitations inherent in the model's architecture. It arises due to the nature of the model's attention mechanism. As the context window grows, the model's ability to recall and distinguish relevant information weakens, blurring its outputs. ## Content-Based Context Rot Content-based context rot results from the accumulation of stale, incorrect, and contradictory information during a session. This information, repeatedly processed by the model, gradually shapes the output. Unlike intrinsic rot, content-based rot is manageable. ## Attention Mechanism and Softmax The model's attention mechanism determines how much each token draws from previous tokens. The softmax function combines attention scores, limiting each token's share. This ensures that even irrelevant information influences the model's output. ## Limits of the Context Window The stated limit of the context window is not a performance cliff; degradation occurs gradually. While the model can still find correct information as the window grows, the clarity of this information diminishes, negatively affecting the signal-to-noise ratio. ## The Impact of Position The position of information in the context window affects the model's performance. Information is better recalled at the beginning and end of the window but weaker in the middle. This can lead to the loss of critical information in long sessions. ## Needle-in-a-Haystack Tests Needle-in-a-haystack tests measure a model's ability to find information in long context windows. While models succeed in simple cases, performance drops earlier in complex scenarios. This indicates that models should operate below the stated context limit. ## Managing Content Rot Managing content rot involves cleaning up unnecessary information accumulated during a session. This makes the model's output more consistent and sharp. Controlling content rot enhances the performance of tools like Claude Code. ## Session Management Tips Session management includes organizing information in the context window and removing irrelevant content. This preserves the model's performance and reduces the effects of context rot. Session management is a critical skill when working with AI models. ## The Model's Memory Limits AI models do not maintain a persistent internal state between turns. The context window is reconstructed in each turn. This highlights the model's memory limits and is one of the causes of context rot. ## Conclusion and Recommendations Context rot is a significant issue that negatively impacts AI model performance. Understanding intrinsic and content-based rot and applying session management techniques improves output quality. This enables more effective use of tools like Claude Code. ## Sources covering this story - https://towardsdatascience.com/governed-context-managing-context-rot-in-claude-code/ — --- _Summary written by Brifing from the original reporting. Copyright in the original article belongs to its publisher; read the full piece at the link above._