Yes, Context Rot is very common in chatbots, especially those designed for long or open-ended conversations. Any chatbot that keeps appending messages to a growing prompt is susceptible to it. Even when the model has a large context window, earlier instructions and facts can lose influence as more turns are added. This makes Context Rot a normal operational issue rather than an edge case.
In simple Q&A chatbots with short interactions, Context Rot may not be noticeable. However, in customer support bots, coding assistants, or agent-style systems that run for dozens of turns, it appears frequently. For example, a chatbot may initially understand that it is helping with a specific product or codebase, but after many follow-up questions, it may start giving generic answers or referencing unrelated features. This happens because the original grounding information is buried under newer conversation text.
From a system design perspective, most production chatbots already assume Context Rot will occur and build around it. Instead of relying solely on raw conversation history, they often re-inject key instructions, summarize past turns, or retrieve fresh context from external memory. Using a vector database such as Milvus or Zilliz Cloud allows the chatbot to dynamically fetch only the most relevant information at each turn, reducing the impact of Context Rot in long conversations.
For more resources, click here: https://milvus.io/blog/keeping-ai-agents-grounded-context-engineering-strategies-that-prevent-context-rot-using-milvus.md