📄 中文摘要
在对话中，大语言模型（LLM）处理上下文时，早期的消息会影响后续消息的解读。经过10-15条消息后，可能会出现几种问题：首先，响应变得更长，模型开始总结之前的消息而不是直接回答；其次，特定性降低，模型开始更多地使用模糊语言，给出不确定的回答；再次，格式出现问题，JSON或结构化输出在最初正常的情况下开始失败；最后，模型可能会忘记之前的约束条件。这种上下文污染的问题往往在用户报告之前无人测试，导致生产环境的潜在破坏。
大语言模型未测试的失败模式（直到它破坏生产环境）
出处: The LLM Failure Mode Nobody Tests For (Until It Breaks Production)
发布: 2026年3月23日
📄 English Summary
The LLM Failure Mode Nobody Tests For (Until It Breaks Production)
Large Language Models (LLMs) process conversation context, where earlier messages influence the interpretation of subsequent ones. After approximately 10-15 messages, several issues can arise: first, responses become longer as the model starts summarizing previous messages instead of providing direct answers; second, specificity decreases as the model hedges more and gives uncertain responses; third, formatting issues occur, with JSON or structured outputs failing after initially working well; finally, the model may forget previous constraints. This context pollution issue often goes untested until users report problems, leading to potential disruptions in production environments.