1.2 你遇到的问题不是工具问题

很多人遇到 AI 协作不顺畅时，第一反应是换工具。

ChatGPT 不够好，换 Claude。Claude 上下文不够长，换 Gemini。Web 端不好用，用 API。Prompt 写不好，上 Prompt 框架。单模型不够，上 Agent 编排。

但问题解决了吗？通常没有。换了一圈，发现新工具也有新问题。

为什么？

因为问题不在工具层面。你换一个模型，AI 仍然不知道你项目的背景。你换一个客户端，对话历史仍然无法跨 session 复用。你写更长的 Prompt，AI 仍然会在长上下文中迷失。

这些问题有一个共同的根：上下文没有被作为基础设施来管理。

模型负责推理，客户端负责交互，Prompt 负责指令——但没有一个组件负责"让 AI 理解它当前所处的整个情境"。这个职责被甩给了用户，用户通过"不断重复"来弥补。

你想想，一个项目里，你跟 AI 说"我们是做电商平台的，技术栈是 Next.js + PostgreSQL"这句话，要说多少次？每一次新对话、每一个新任务、每一次换工具，你都要说一遍。这不合理。

工具解决的是"怎么问"的问题，但你没解决的是"AI 需要知道什么"的问题。

后者比前者重要得多。一个知道所有背景的 AI，用最原始的模型，也能给出不错的答案。一个什么都不知道的 AI，配最先进的模型，也可能答非所问。

所以，不要在工具层面找答案。

先把上下文管好。然后你会发现，用什么工具、什么模型、什么 Prompt 框架，都变得简单了。因为 AI 已经知道它需要知道的一切，你要做的只是问问题而已。

这不是一个技术问题，这是一个结构问题。技术问题有标准答案，结构问题需要你自己设计。

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