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我一直在日常工作中尝试使用 AI 编码助手，并遇到了一个反复出现的问题：我一直在重复相同的工程原则。比如验证输入、谨慎使用新依赖项，或者遵守某些产品约束。通常的解决方案是提示或规则。在使用这两种方法一段时间后，我感觉都不太合适。 - 提示在每次任务后都会消失。 - 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。 - 有些原则是个人偏好，我不想在项目层面强制执行。 - 另一些根本不是“规则”，而是关于产品约束和过去权衡的知识。这促使我尝试为 AI 助手创建一个单独的“记忆”层。不是聊天记录，而是小的、原子的知识片段：决策、约束和可以根据需要检索的重复出现的原则。在开始认真使用它之后，一些事情变得显而易见： - 模糊的记忆会导致模糊的行为 - 冗长的记忆会污染上下文 - 重复的条目会使检索效果变差 - 许多问题只有在您每天真正依赖助手时才会出现当上下文正确时，AI 在执行方面表现出色。但是，决定应该记住什么、应该拒绝什么，以及何时可预测性比聪明才智更重要，仍然需要人为判断。很好奇其他人是如何处理这个问题的。您在使用 AI 编码助手时，主要依赖提示、规则，还是某种形式的持久知识？

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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?

思考用于 AI 编码 agent 的记忆