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Context Engineering最近因为aj变的很火，那他是什么，与prompt什么关系呢？有一个视频做了很好的解释，最核心的一点是“ Prompt Engineering 是Context Engineering的一部分，如果 Prompt Engineering 正在编写一个出色的指令......上下文工程决定该指令之前和之后会发生什么——记住什么，从内存或工具中提取什么，整个事情是如何构建的。”，具体如下：
原文作者：Mehul Gupta
YouTubeContext Engineering vs Prompt Engineering
Context Engineering vs Prompt Engineering
Andrej Karpathy has recently coined a new term Context Engineering after Vibe Coding #ai #chatgpt #promptengineering #vibecoding
YouTubeContext Engineering 包含了prompt的设计，它是面向的大模型对话交互的设计框架考量。
Prompt Engineering focuses on what to say to the model at a moment in time.
Context Engineering focuses on what the model knows when you say it — and why it should care.
If Prompt Engineering is writing a brilliant instruction…Context Engineering is deciding what happens before and after that instruction — what’s remembered, what’s pulled from memory or tools, how the whole thing’s framed.
如果 Prompt Engineering 正在编写一个出色的指令......上下文工程决定该指令之前和之后会发生什么——记住什么，从内存或工具中提取什么，整个事情是如何构建的。
Bad Prompt Engineering: 糟糕的提示工程：
- Output tone is wrong 输出错误
- Instructions get ignored 指令被忽略
- Model acts like it’s drunk 模特表现得像傻子一样
- You spend hours tweaking commas and synonyms 您花费数小时调整逗号和同义词
Bad Context Engineering: 不良环境工程：
- Model forgets why it’s even in the conversation 模型甚至忘记了为什么它出现在对话中
- Prompt gets drowned in noise 提示词被噪音淹没
- Output is generic, unhinged, or misleading 输出是通用的、不合理的或具有误导性的
- RAG breaks, memory leaks, tool chaining fails RAG 中断、内存泄漏、工具链接失败
- Protects your prompt. You can write the best instruction ever, but if it’s lost at token 12,000 behind three FAQs and a JSON blob, it won’t matter.
保护您的提示。 您可以编写有史以来最好的指令，但如果它在三个常见问题解答和一个 JSON blob 后面丢失了 12,000 个令牌，那也没关系。
- Structures everything around the prompt. Memory, retrieval, system prompt — all exist to support the prompt’s clarity and priority.
围绕提示符构建所有内容。 内存、检索、系统提示符 — 所有这些都是为了支持提示的清晰度和优先级。
- Handles scale. You don’t need to keep engineering prompts for every variation. You inject structured context that adapts to different users/tasks.
处理缩放。 您无需为每个变体保留工程提示。您注入适应不同用户 / 任务的结构化上下文。
- Manages constraints. Token limits? Latency? Costs? Context Engineering decides what gets dropped and what stays.
管理约束。 令牌限制？延迟？成本？上下文工程决定丢弃哪些内容，保留哪些内容。
- Mindset: Prompt Engineering is about crafting clear instructions; Context Engineering is about designing the entire flow and architecture of a model’s thought process.
心态：Prompt Engineering 是关于制定明确的指令;上下文工程是关于设计模型思维过程的整个流程和架构。
- Scope: Prompt Engineering operates within a single input-output pair; Context Engineering handles everything the model sees — memory, history, tools, system prompts.
范围：Prompt Engineering 在单个输入-输出对中运行;上下文工程处理模型看到的所有内容 — 内存、历史记录、工具、系统提示。
- Repeatability: Prompt Engineering can be hit-or-miss and often needs manual tweaks; Context Engineering is designed for consistency and reuse across many users and tasks.
重复性：Prompt Engineering 可能是偶然的，通常需要手动调整;上下文工程旨在实现许多用户和任务之间的一致性和重用性。
- Scalability: Prompt Engineering starts to fall apart when scaled — more users = more edge cases; Context Engineering is built with scale in mind from the beginning.
可扩展性：Prompt Engineering 在扩展时开始分崩离析 — 更多的用户 = 更多的边缘情况;环境工程从一开始就考虑到了规模。
- Precision: Prompt Engineering relies heavily on wordsmithing to get things “just right”; Context Engineering focuses on delivering the right inputs at the right time, reducing the burden on the prompt itself.
精度：Prompt Engineering 在很大程度上依赖于文字加工来使事情变得“恰到好处”;上下文工程专注于在正确的时间提供正确的输入 ，从而减轻提示本身的负担。
- Debugging: Prompt Engineering debugging is mostly rewording and guessing what went wrong; Context Engineering debugging involves inspecting the full context window, memory slots, and token flow.
调试：Prompt Engineering 调试主要是改写和猜测出了什么问题;上下文工程调试涉及检查完整的上下文窗口、内存插槽和令牌流。
- Tools Involved: Prompt Engineering can be done with nothing but ChatGPT or a prompt box; Context Engineering needs memory modules, RAG systems, API chaining, and more backend coordination.
涉及的工具： 提示工程只能用 ChatGPT 或提示框来完成;上下文工程需要内存模块、RAG 系统、API 链接和更多的后端协调。
- Risk of Failure: When Prompt Engineering fails, the output is weird or off-topic; when Context Engineering fails, the entire system might behave unpredictably — including forgetting goals or misusing tools.
故障风险： 当 Prompt Engineering 失败时，输出很奇怪或偏离主题;当情境工程失败时，整个系统的行为可能会不可预测，包括忘记目标或滥用工具。
- Longevity: Prompt Engineering is great for short tasks or bursts of creativity; Context Engineering supports long-running workflows and conversations with complex state.
长寿：Prompt Engineering 非常适合短期任务或创造力的爆发;上下文工程支持长时间运行的工作流和具有复杂状态的对话。
- Effort Type: Prompt Engineering is like creative writing or copy-tweaking; Context Engineering is closer to systems design or software architecture for LLMs.
努力类型：Prompt Engineering 就像创意写作或文案调整;上下文工程更接近于 LLM 的系统设计或软件架构。
