# Is Context Rot common in chatbots? Source: https://zilliz.com/ai-faq/is-context-rot-common-in-chatbots Author: zilliz 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](https://milvus.io/) or [Zilliz Cloud](https://zilliz.com/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: