---
title: "什么是 Prompt Engineering？"
canonical: https://wavect.io/zh/glossary/prompt-engineering/
language: zh
description: "编写你发送给 LLM 的指令和上下文，使它产出你真正想要的输出。这是真正的工程，而不是某种魔法咒语。"
image: "https://wavect.io/img/general/bak/open_graph_preview.jpg"
---

技术

# Prompt Engineering

编写你发送给 LLM 的指令和上下文，使它产出你真正想要的输出。这是真正的工程，而不是某种魔法咒语。

最近审阅: 2026-06-02 审阅人 [Kevin Riedl](/zh/team/kevin-riedl/) [wiki ↗](https://www.wikidata.org/wiki/Q139796365)

Prompt 工程是构建你发送给模型内容的实践：任务描述、示例、约束、输出格式和上下文。在你告诉它之前，模型完全不知道你想要什么，而你怎么告诉它会极大地改变结果。含糊的 Prompt 得到含糊的回答。带示例和明确输出模式的精确 Prompt 得到你真正能上线的东西。

炒作把它包装成一种神秘技能。现实更平凡也更有用：它是迭代工程。你写一个 Prompt，针对真实案例测试它，看它在哪里失败，收紧指令或补充示例，再次衡量。系统 Prompt（位于每条用户消息之上的常驻指令）是大部分持久行为所在之处，所以真正的工作流向那里。

这里是诚实的部分：Prompt 工程是真实的，但它不是职业护城河。这些技巧一周就能学会，而模型也在不断变得更善于理解草率的 Prompt。无法被商品化的，是知道该把模型指向哪个问题、把它接入一个真实系统，以及评估输出是否好到足以信任。这就是工程，也是我们在 [人工智能](/zh/services/artificial-intelligence/) 下所做的事。

便宜的部分与昂贵的部分如何分野，举个实际例子：一个团队让一个 Demo Prompt 在五个精挑细选的输入上跑得很漂亮，就宣布功能完成、直接上线。到了生产环境，同一个 Prompt 在没人测过的、杂乱的真实输入上垮掉，因为没有一层 [RAG](/zh/glossary/rag/) 给它喂正确的上下文，也没有一套评估装置来衡量它出错的频率。Prompt 从来就不是难的部分。知道模型的 [上下文窗口](/zh/glossary/context-window/) 预算、把检索接进去、并在真实案例上衡量输出质量，才是真正能上线一个可靠功能的工作。对任何把「Prompt 工程」当作独立产品来卖的人保持警惕：Prompt 是便宜的部分，昂贵的部分是它周围的一切，检索、评估、护栏，以及把一个巧妙的 Prompt 变成可靠功能的集成。

// FAQ

## 常见问题

### Prompt 工程到底是什么？

构建你发送给 LLM 的指令、示例和上下文，使它返回可用的输出。它是迭代的测试与改进，而不是某个能解锁模型的秘密短语。

### Prompt 工程是一份真实的工作吗？

它是一项真实的技能，但作为独立职位头衔很弱。技巧很快就能学会，而模型也在不断进步。持久的价值在于围绕 Prompt 的系统设计和评估，而不在 Prompt 本身。

### 什么是系统 Prompt？

位于每条用户消息之上、定义模型默认行为方式的常驻指令集。大多数可靠的行为在这里被塑造，因此它获得了最多的工程关注。

相关术语

- [LLM](/zh/glossary/llm/)
- [RAG](/zh/glossary/rag/)
- [Context Window](/zh/glossary/context-window/)

相关服务

- [人工智能](/zh/services/artificial-intelligence/)

相关案例研究

- [PromptID](/zh/case-studies/promptid/)

相关指南

- [如何为 MVP 选择技术栈](/zh/software-development-guide/how-to-choose-a-tech-stack-for-mvp/)

聊聊

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