Users usually notice Context Rot through behavioral changes in the chatbot rather than obvious errors. One common sign is that the chatbot starts ignoring or contradicting earlier instructions. For example, a user may clearly specify a constraint like “answer using only the provided data,” but later in the conversation the chatbot begins introducing information that was never mentioned.
Another sign is loss of consistency. The chatbot may redefine terms that were already agreed upon or give answers that no longer match earlier explanations. For instance, in a technical discussion, the model might initially use a specific API version or architecture, then later respond as if a different version is being discussed. These shifts are subtle but frustrating, especially for users who expect continuity.
From a user’s point of view, Context Rot feels like the chatbot is “getting confused” or “forgetting the point.” Under the hood, the model is still seeing all previous text, but its attention has shifted. This is why well-designed systems rely on structured context management and external retrieval from systems like Milvus or Zilliz Cloud, rather than trusting the raw conversation history to remain coherent indefinitely.
For more resources, click here: https://milvus.io/blog/keeping-ai-agents-grounded-context-engineering-strategies-that-prevent-context-rot-using-milvus.md