Context Rot cannot be fully avoided, but it can be significantly reduced with good system design. Because it arises from fundamental properties of attention-based models, no prompt or model setting can eliminate it entirely. However, developers can control how quickly it appears and how severe its effects are.
The most effective mitigation techniques fall under context engineering. These include summarizing older context, explicitly reasserting system instructions, ranking retrieved documents, and limiting prompt growth. Instead of keeping everything in the prompt, many systems store long-term knowledge externally and retrieve only what is needed for the current turn. This approach reduces noise and keeps the model focused.
Vector databases play a key role here. By storing knowledge in a system like Milvus or Zilliz Cloud, applications can dynamically select the most relevant context instead of relying on accumulated history. While Context Rot is inevitable at some level, careful retrieval and context management make it manageable in real-world systems.
For more resources, click here: https://milvus.io/blog/keeping-ai-agents-grounded-context-engineering-strategies-that-prevent-context-rot-using-milvus.md