Prompt Patterns
开源中文参考书，用软件设计模式的思路系统化编写 AI Prompt
如果你已经写过几十条 Prompt 却始终靠感觉调整，这本书值得从头读一遍：四大基础模式加上格式转换、表格输出、反向 Prompt 等子模式，给了一套可以命名的词汇表，团队讨论 Prompt 时不再只能说“再改改”。它最大的价值在概念类比章节，把设计模式的七要素直接套用到 prompt 定义上，这是大多数提示工程教程没有的角度。局限同样明显：它是静态网页文档，没有交互式练习或验证，想要能直接调用的 Prompt 助手应该去看 Click Prompt 这类工具，想要企业级管控则这本书帮不上忙。建议边读边在 ChatGPT 或 Claude 里跑一遍书中的例子。
Verified 1d ago · liveness 59/100 · cite: rightaichoice.com/tools/prompt-patterns
- 熟悉设计模式、想把 Prompt 工程化的开发者
- 需要一套结构化 Prompt 词汇表的提示工程师
- 做自然语言处理、想参考模式化分类的研究者
- 接受中文内容、愿意自己动手验证的初学者
- 想要即时交互式 Prompt 助手的人
- 需要 API、集成或企业级管控的团队
- 只读英文、不接受社区翻译滞后的读者
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Skip Prompt Patterns if you want an interactive prompt builder or API-backed prompt management rather than a Chinese-language reference book you read and then apply yourself.
Prompt Patterns's pricing fits teams whose volume aligns with the published tiers. Compare against the alternatives listed below for stage-specific value.
In short
Prompt Patterns — 开源中文参考书，用软件设计模式的思路系统化编写 AI Prompt. Best for 熟悉设计模式、想把 Prompt 工程化的开发者, 需要一套结构化 Prompt 词汇表的提示工程师, 做自然语言处理、想参考模式化分类的研究者. Free to use.
What people actually say about Prompt Patterns — is it worth it?
We ran a structured research pass across product reviews, community discussions, and post-purchase forum threads to surface the patterns vendors won't publish themselves. Below: the recurring strengths, the hidden costs people mention most, and the cohort that consistently regrets adopting this tool.
69 mentions across 5 sources (Hacker News, YouTube, Bluesky, GitHub, Lemmy) · researched Jul 14, 2026.
Average across the 5 sources that answered — each source counts once, not each post.
- +Free and open-source with permissive license.
- +Structured design-pattern approach makes prompting systematic.
- +GitHub community with 3k+ stars and active issue discussion.
- +Covers 4 essential patterns: specific instruction, template, agent, example.
- +Linked with Click Prompt for hands-on practice.
- −Content is only in Chinese, limiting global reach.
- −Only 4 patterns — too shallow for advanced users.
- −Pattern boundaries are fuzzy; some overlap causes confusion.
- −No built-in solution for multi-turn conversation memory.
- −Lacks complex, production-ready integration examples.
- • No hidden costs — all content is free and open-source.
Viability Score
How well maintained and how widely used is Prompt Patterns? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: September 2026
How we score →Key Features
- 免费在线读完全部章节的静态网页书籍
- 四大基础模式：特定指令、指令模板、代理模式、示例模式
- 子模式：格式转换、表格输出、反向 Prompt、再生成模式
- 以设计模式七要素（名称/问题/解决方案/效果/适用性/结构图/参考）组织每个模式
- 核心方法为概念类比，将设计模式要素迁移到 prompt
- Output Driven 原则：在提示开头写明上下文、期望结果、长度与格式
- 最佳实践：zero-shot、few-shot 优先，不足再微调
- ChatGPT Simple Cheatsheet 可下载 PDF 与 PPTX
- 代理模式示例：act as 角色扮演（餐饮客服、Linux 终端）
- 指令模板示例：STAR 原则结构化总结
- 开源协作：Issue 报告、Pull Request 改进章节、申请翻译
- 关联 Click Prompt 开源工具（GitHub: prompt-engineering/click-prompt）
- GitHub 仓库托管，内容可被 fork 和二次分发
- 文内示例涵盖 Stable Diffusion 的 negative prompt 用法
About Prompt Patterns
Prompt Patterns 是 Phodal 发起的开源中文参考书，把软件设计模式的概念搬进提示工程：每个模式按“模式名称、问题描述、解决方案、效果、适用性、结构图、参考”七要素组织。书里给出四种基础模式——特定指令（By specific）、指令模板（Instruction Template）、代理模式（By proxy）、示例模式（By demonstration），并整理了格式转换、表格输出、反向 Prompt、再生成等子模式。核心方法是概念类比：先让 AI 回答“设计模式的要素是哪些”，再让它把这套要素套用到 prompt 上（书中 design-pattern.analogy 章节）。最佳实践章节给出的顺序是 zero-shot、few-shot 优先，效果不够再补充少量样本，仍不行才考虑微调。全书以静态网页免费在线阅读，配套 ChatGPT Simple Cheatsheet 可下载 PDF 和 PPTX，并指向开源工具 Click Prompt（GitHub 仓库 prompt-engineering/click-prompt）用于实践。贡献方式是提 Issue、发 Pull Request 或申请翻译。适合熟悉设计模式、想把 Prompt 当成可复用资产来管理的开发者和提示工程师，不适合需要交互式 Prompt 助手或 API 能力的用户。
Behind the Verdict
这本书解决的是一个被大多数提示工程教程跳过的前置问题：怎么给 Prompt 命名和分类。软件工程师对“模式名称—问题—解决方案—效果”这套结构很熟，Phodal 把它整体迁移过来，于是“如何准备训练数据”这类模糊问题变成了有边界的模式条目。四种基础模式覆盖了最常见的输入方式：给关键词（特定指令）、给明确指令格式（指令模板）、给角色身份（代理模式，也就是 ChatGPT 里的 act as，书中举了餐饮客服和 Linux 终端两个例子）、给示例（示例模式）。子模式则更贴近日常：用 """ """ 包裹要求输出 markdown，指定表格字段生成结构化表格，用反向 Prompt 排除不想要的元素（书中提到在 Stable Diffusion 中配置 negative prompt 的实例），以及再生成模式。 Output Driven 是书里最实用的一条原则：把上下文、期望结果、长度和格式样式全部写在提示开头，比如“用 100 字总结一下 ChatGPT”。指令模板章节的 STAR 例子（情境/任务/行动/结果）演示了如何用固定字段把一段杂乱的工作总结变成结构化输出，甚至细到要求中英文之间插空格。 弱点也清楚。它是一本书，不是产品：没有交互式练习、没有验证环节、没有 API，读到模式之后能不能用好，取决于你愿不愿意自己找场景试。内容以中文为主，其他语言依赖社区翻译，非中文读者现在拿不到完整版本。静态网页的形态也意味着它不会主动告诉你哪条模式在新模型上已经失效。 定位上它和 Click Prompt 是互补的：这本书提供方法论和词汇，Click Prompt 提供了把模式跑起来的工具入口。对于已经在用设计模式写代码的团队，这是把 Prompt 纳入工程规范的一条低成本路径；对于只想找个对话框问问题的人，这本书会显得过于啰嗦。
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Real-world workflow fit
Concrete scenarios for the personas Prompt Patterns actually fits — and what changes day-one when you adopt it.
先把 20 条线上 Prompt 按书中七要素逐条归位，标出哪些属于特定指令、哪些属于示例模式，再针对归类困难的那几条去查子模式章节
Outcome: Prompt 有了统一命名和分类，Code Review 里讨论 Prompt 时可以直接引用模式名，改动理由可追溯
用 Output Driven 原则和 STAR 指令模板，给团队写一份 Prompt 提交模板，要求写清上下文、期望结果、长度和格式样式
Outcome: 新人第一周就能写出格式稳定的 Prompt，减少反复返工的沟通成本
从 zero-shot 开始试，效果不好再按书里的 few-shot 示例补少量样本，仍不满意才考虑微调，同时下载 PDF 速查表放在手边
Outcome: 按书里的顺序逐级加码，避免一上手就堆砌复杂技巧
Use Cases
- 系统学习 Prompt 编写模式，让 AI 输出更可控
- 用设计模式类比快速搭建复杂 Prompt 结构
- 查子模式（格式转换、表格输出、反向 Prompt）控制输出格式
- 下载 PDF/PPTX 速查表，日常写 Prompt 时随手对照
- 为团队建立统一的 Prompt 命名与设计规范
- 把书里的模式交给 AI 做概念类比，生成新的模式条目
Limitations
这是一本以静态网页形式发布的免费开源参考书，没有交互式练习，也没有对 Prompt 效果的自动验证，读完之后能否用好取决于你自己是否找场景实践。内容以中文为主，其他语言版本依赖社区翻译，进度不由官方控制。书中示例涉及 Stable Diffusion 等具体模型参数，但不同模型对同一模式的响应并不一致，需要自行调整。作为文档项目，它没有 API 或可直接集成的接口，Click Prompt 是文中另行指向的实践工具，不是本书内置功能。
as of 2026-09-28
Verification history
We have re-verified Prompt Patterns 8 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-checked, vendor evidence unchanged
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-checked, vendor evidence unchanged
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
Showing the 6 most recent of 8 verification passes.
Free to cite with attribution — this page re-verifies continuously.
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Where the pricing makes sense
The company stage and team size where Prompt Patterns's pricing actually pencils out — and where peers do it cheaper.
Prompt Patterns's pricing fits teams whose volume aligns with the published tiers. Compare against the alternatives listed below for stage-specific value.
Setup time & first value
How long it actually takes to get something useful out of Prompt Patterns — broken out by persona, not the marketing-page minute.
作为在线阅读的参考书，打开网页即可开始读，按章节顺序走一遍大约几小时。若目的是改造现有 Prompt，先挑书中四大基础模式和子模式两节对照自己的用例，通常一个下午能完成一轮归类；下载 PDF/PPTX 速查表后可以长期随手查阅。实际见效时间取决于你愿不愿意拿真实任务去验证书中模式。
Switching to or from Prompt Patterns
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →从零散收藏的 Prompt 片段：按七要素把每条归入特定指令、指令模板、代理或示例模式，再逐步替换掉无结构的写法
- →从通用提示工程教程：把教程里的技巧对应到书中的模式名称，建立可检索的词汇表
- ↗转向 Click Prompt 等可执行工具：把书中模式作为模板输入，由工具负责拼装和调用
- ↗转向团队自建 Prompt 库：以书中七要素作为字段结构，迁移到内部文档或代码仓库
Resources & Guides
Tutorials & Learning
YouTube returned 3 videos for “Prompt Patterns”, and we withheld 2: 2 did not mention Prompt Patterns. Showing the 1 we can prove is about Prompt Patterns.
Official links
Tools that pair well with Prompt Patterns
Common stack mates teams adopt alongside Prompt Patterns, with the specific reason each pairing earns its keep.
Claude
Claude is Anthropic's AI assistant for long-document analysis, coding, and agentic work in chat and Cowork.
ReadMe
ReadMe is an API documentation platform with bi-directional Git sync and built-in AI writing assistants.
Replit Agent
Replit Agent turns a plain-English prompt into a runnable full-stack app inside your browser, then deploys it to a live URL.
Featured Head-to-Head Comparisons
Prompt Patterns vs Surge Ai
For a solo prompt engineer or researcher wanting free, structured patterns to improve prompts, Prompt Patterns is a clear choice. But if you're building frontier models and need expert human feedback, red teaming, and benchmarks that measure real-world reasoning (as Anthropic uses), Surge AI's domain-expert workforce and latest benchmarks make it indispensable—though costs are undisclosed and require a budget.
Prompt Patterns vs Praktika
If your goal is to improve conversational fluency in a foreign language with on-demand practice, Praktika's AI tutors offer a structured yet flexible experience, validated by recent case studies. But if you're looking to systematize your prompt writing for AI coding, Prompt Patterns provides a free, open-source methodology grounded in design patterns. Choose based on your domain: language learning vs. prompt engineering.
Alternatives to Prompt Patterns
View allClaude
Claude is Anthropic's AI assistant for long-document analysis, coding, and agentic work in chat and Cowork.
ReadMe
ReadMe is an API documentation platform with bi-directional Git sync and built-in AI writing assistants.
Replit Agent
Replit Agent turns a plain-English prompt into a runnable full-stack app inside your browser, then deploys it to a live URL.
Frequently Asked Questions
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