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我在日常工作中尝试使用AI编码助手，遇到了一个反复出现的问题：我不断重复相同的工程原则。比如验证输入、谨慎处理新的依赖关系，或者遵循某些产品约束。通常的解决方案是提示或规则。但在使用这两者一段时间后，我发现它们都不太合适。 - 提示在每个任务完成后就消失了。 - 规则仅在狭窄的上下文中触发，通常与特定文件或模式相关。 - 一些原则是个人偏好，而不是我希望在项目层面强制执行的内容。 - 还有一些根本不是“规则”，而是关于产品约束和过去权衡的知识。这让我开始尝试为AI助手创建一个独立的“记忆”层。不是聊天记录，而是小而原子的知识片段：决策、约束和可以在相关时检索的重复原则。一旦我认真使用它，有几件事变得显而易见： - 模糊的记忆导致模糊的行为 - 长期记忆会污染上下文 - 重复条目使检索变得更糟 - 许多问题只有在你每天依赖这个助手时才会显现出来AI在上下文正确时执行得很好。但决定哪些内容应该被记住、哪些应该被拒绝，以及何时可预测性比聪明才智更重要，仍然需要人类的判断。我很好奇其他人是如何处理这个问题的。在与AI编码助手合作时，你主要依赖提示、规则，还是某种形式的持久知识呢？

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I’ve been experimenting with AI coding agents in real day-to-day work and ran into a recurring problem, I keep repeating the same engineering principles over and over.Things like validating input, being careful with new dependencies, or respecting certain product constraints. The usual solutions are prompts or rules.After using both for a while, neither felt right. - Prompts disappear after each task. - Rules only trigger in narrow contexts, often tied to specific files or patterns. - Some principles are personal preferences, not something I want enforced at the project level. - Others aren’t really “rules” at all, but knowledge about product constraints and past tradeoffs.That led me to experiment with a separate “memory” layer for AI agents. Not chat history, but small, atomic pieces of knowledge: decisions, constraints, and recurring principles that can be retrieved when relevant.A few things became obvious once I started using it seriously: - vague memory leads to vague behavior - long memory pollutes context - duplicate entries make retrieval worse - many issues only show up when you actually depend on the agent dailyAI was great at executing once the context was right. But deciding what should be remembered, what should be rejected, and when predictability matters more than cleverness still required human judgment.Curious how others are handling this. Are you relying mostly on prompts, rules, or some form of persistent knowledge when working with AI coding agents?

关于人工智能编码代理的记忆思考