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我最近在使用Cursor编程时遇到了一个常见问题。最初，我们通常会给AI一些明确的约束，例如：不修改数据库架构不修改某些API只允许修改前端逻辑某些函数名称不能更改在对话开始时，AI通常能够很好地遵循这些规则。然而，随着对话的延长，比如达到40,000或50,000个标记，一个常见问题出现了：AI逐渐“忘记”之前提到的限制。例如：最开始你说“不要修改数据库，”但经过几轮对话后，它突然建议：“我们可以通过修改数据库结构来解决这个问题。”我之前问过其他人如何解决这个问题，有人建议：在项目中写一个important.md或规则文件，以便AI每次都能读取。这个方法确实有一定效果，但在实际开发中仍然会出现问题。例如：最开始你说“不要碰数据库A，”但后来项目中添加了数据库B。如果你没有及时更新markdown文件，AI可能会意外修改你不想更改的内容。因此，我最近创建了一个小型实验工具，主要是为了应对AI编程助手中的“约束漂移”问题。

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I recently encountered a common problem while coding with Cursor.Initially, we usually give the AI some explicit constraints, such as:Don't modify the database schemaDon't modify certain APIsOnly allow modifications to the front-end logicSome function names cannot be changedAt the beginning of the conversation, the AI generally follows these rules well.However, as the conversation lengthens, for example, to 40,000 or 50,000 tokens, a common problem arises:The AI gradually "forgets" the previously mentioned restrictions.For example:Initially you say "Don't modify the database,"but after a few rounds, it suddenly suggests:"We can solve this problem by modifying the database structure."I've asked others how to solve this before, and some suggested:Write an important.md or rule file in the project so the AI reads it every time.This method does have some effect, but problems still arise in actual development.For example: Initially you say "Don't touch database A,"but later database B is added to the project.If you don't update the markdown file in time, the AI might accidentally modify things you didn't intend to change.So I recently created a small experimental tool, mainly to solve the "constraint drift" problem in AI programming assistants.

我建立了一个小实验，以检测人工智能编码助手何时出现偏差。