# Prompt-Engineering-Guide-zh-CN: A Chinese Translation of the dair-ai Prompt Engineering Guide

> yunwei37/Prompt-Engineering-Guide-zh-CN is a Simplified Chinese fork of the dair-ai Prompt Engineering Guide, built as a Nextra site. It is a reading resource, not a library, and its licence file is not a recognized SPDX identifier.

**yunwei37/Prompt-Engineering-Guide-zh-CN** — 🐙 关于提示词工程（prompt）的指南、论文、讲座、笔记本和资源大全（自动持续更新）

- Repository: https://github.com/yunwei37/Prompt-Engineering-Guide-zh-CN
- Website: https://github.com/dair-ai/Prompt-Engineering-Guide
- Stars: 1,066 · Forks: 99
- Language: MDX
- License: NOASSERTION
- Published: 2026-09-14 · Updated: 2026-09-14 · Language: en
- Canonical page: https://hysenlabs.com/projects/yunwei37-prompt-engineering-guide-zh-cn

## What the Chinese fork of the Prompt Engineering Guide actually is

The README describes the project as a collection of recent papers, learning guides, lectures, references and tools related to prompt engineering, and states that it was created because of the high interest in developing LLMs. The repository name and the description both mark it as the zh-CN edition: a Chinese-language version of the dair-ai Prompt Engineering Guide, whose homepage is listed as https://github.com/dair-ai/Prompt-Engineering-Guide. So the unit of value here is text, not code you import. The guides/ directory holds nine Markdown documents covering introduction, basic prompting, advanced prompting, applications, ChatGPT, adversarial prompting, reliability and miscellaneous topics. The pages/ directory holds MDX files for papers, tools, datasets and additional readings. If you are looking for a pip package, a Node module or a service endpoint, this is the wrong repository; the only runnable artifact is the documentation site itself.

## How the Nextra site is assembled from guides and pages

The build is a Next.js application using Nextra as the documentation theme. package.json lists next, nextra, nextra-theme-docs, react, react-dom and @vercel/analytics as dependencies, with typescript in devDependencies, and defines three scripts: dev, build and start. theme.config.tsx at the repository root configures the Nextra docs theme, and next.config.js holds the Next configuration; components/ supplies local React components used inside MDX pages. The content flow is therefore: Markdown in guides/, MDX in pages/, both rendered through the Nextra theme, with the site metadata taken from theme.config.tsx. The lecture/ directory carries the slides PDF referenced in the README, and notebooks/ carries the accompanying notebook. One detail worth flagging: package.json still declares the project name as nextra-docs-template and points its repository field, author and bugs URL at shuding/nextra-docs-template, the upstream template. That is normal for a fork that never renamed the manifest, but it means npm metadata about this repository will describe the template rather than the guide.

## Installing and building the guide locally

The README does not give installation steps; it points readers to the hosted web version at promptingguide.ai and to the guide files themselves. What the repository does provide is a standard Next.js toolchain, so a local build follows from package.json. The lockfiles in the tree are both package-lock.json and pnpm-lock.yaml, and the scripts are the Next.js defaults. From a clone of the repository, install dependencies and start the development server:

```bash
pnpm install
pnpm dev
```

The dev script runs next dev, so the terminal prints the local URL Next.js serves on, by default port 3000. Open that URL and you should see the Nextra documentation layout with the Chinese guide pages in the sidebar. For a production build, the build script runs next build and the start script runs next start:

```bash
pnpm build
pnpm start
```

If you prefer npm, the equivalent commands are npm install, npm run dev, npm run build and npm run start; the script names are the same because they come from package.json. There is no separate content-generation step, so editing a file under guides/ and saving it is the whole authoring loop in dev mode.

## The translation is a fork, and forks drift

The README's announcements section credits the upstream project: it links to the web version at promptingguide.ai, a course built with Sphere, a one-hour lecture video with an accompanying notebook and slides, and a Hacker News first place from 2023-02-21. None of that is this repository's own work; it is inherited context. The practical consequence is that the Chinese text tracks the upstream English guide only as far as the fork has been kept in sync, and nothing in the repository describes a sync process, a translation workflow, or a diff against upstream. There are no releases in the repository, so there is no version number you can pin the Chinese content to, and no changelog to tell you which upstream revision a given page corresponds to. If your work depends on a specific technique being described exactly as the upstream guide describes it, read both. The last push was on 2026-05-13, which is more than four months before today, so treat the content as a snapshot rather than a feed.

## Prompt engineering for LLMs as a reading subject, not a dependency

The README frames the discipline directly: prompt engineering is described as a relatively new field for developing and optimizing prompts to use language models efficiently, and the stated audiences are researchers who use it to improve LLM performance on tasks such as question answering and arithmetic reasoning, and developers who use it to design prompts that interact with LLMs and other tools. That framing tells you what adoption looks like here. You do not add this repository to a build pipeline; you read guides/prompts-basic-usage.md before writing your first few-shot prompt, or guides/prompts-adversarial.md before you worry about prompt injection. The pages/ directory is a bibliography and tool index rather than a runtime. Teams that want a shared internal reference can vendor the Markdown, but they inherit the same licence question discussed below. Teams that want a tested prompt library with typed inputs will not find one in this tree.

## Where a fork of a guide stops being the right tool

The clearest failure mode is expecting freshness. The README's own update banner says the project is automatically and continuously updated, but the repository has no releases and the manifest is unchanged from the Nextra template, so there is no signal that distinguishes a translated page from a stale one. A second limitation is scope: this is the Chinese edition of one guide, so if your team works in English, or needs the upstream English text as the reference, you should read dair-ai/Prompt-Engineering-Guide directly rather than a downstream copy. A third is that the repository is a documentation site, so any question about model behaviour, token limits or API parameters has to be answered from the model provider's own documentation; the guide describes techniques, not vendor endpoints. For a team that wants a maintained, versioned prompt-engineering reference with a release history, this repository does not offer one.

## How it differs from the upstream dair-ai guide and from vendor guides

The direct alternative is dair-ai/Prompt-Engineering-Guide, the English original this repository forks. The difference is language and maintenance surface: upstream is the source of the content and the place where new techniques, papers and tools first appear, while this fork carries the Simplified Chinese rendering and the same Nextra site structure. Choosing between them is a question of which language your readers work in, not of features. A second class of alternative is the vendor-produced material that shows up in searches around this topic, such as Google's prompt engineering guide and OpenAI's prompt engineering guide; those are written by the model vendors and describe their own models' behaviour and recommended patterns. The trade-off is coverage versus specificity: a community guide like this one spans many papers and techniques across model families, while a vendor guide is narrower but tied to a concrete API and its current parameters. Neither replaces the other, and the README does not claim otherwise.

## Licence and the cost of keeping a fork alive

The repository's licence is reported as NOASSERTION, and LICENSE.md is present at the root but is not a recognized SPDX identifier in the repository metadata. The README does not state reuse terms for the translated text, and it does not describe how the translation is licensed relative to the upstream guide. That is the thing to resolve before you redistribute the content, embed it in a product, or republish it internally at scale; ask the repository owner, and check the upstream project's terms as well, since a translation of someone else's text carries the original rights. On maintenance cost: the site is a standard Next.js and Nextra app with two lockfiles, so upgrading means bumping next, nextra and nextra-theme-docs and re-checking theme.config.tsx against any breaking theme changes. The content itself has no build step, which keeps the cost low, but the absence of releases means you cannot express an upgrade as a version bump. This is not legal advice; it is a description of what the repository does and does not tell you.

## Conclusion

Adopt it if your team reads Chinese and you want the dair-ai prompt engineering material in that language, cloned locally and built with pnpm dev. Do not adopt it expecting an installable package, an API, or a maintained fork with its own release cycle; the package.json is still named nextra-docs-template, the repository publishes no releases, and the licence file is not a recognized SPDX identifier. Verify first whether the upstream dair-ai guide has moved ahead of this fork, and confirm the licence terms with the repository owner before shipping the content inside a commercial product.

## FAQ

### What exactly is prompt engineering?

The README describes prompt engineering as a relatively new discipline for developing and optimizing prompts so that language models can be used efficiently across applications and research topics. It adds that the skill helps in understanding the capabilities and limitations of large language models.

### Is prompt engineering difficult to learn?

The repository does not rate the difficulty of learning prompt engineering. It does present a structured path through the guides directory, from prompts-intro.md and prompts-basic-usage.md to prompts-advanced-usage.md and prompts-applications.md, which is the sequence a beginner would follow.

### Can you provide a guide for beginners to learn prompt engineering?

The repository's guides/ directory is that path: it lists a prompt engineering introduction, basic prompting, advanced prompting, applications, ChatGPT, adversarial prompting, reliability and miscellaneous topics as separate Markdown documents. The README also links a one-hour lecture video with an accompanying notebook and slides.

## Sources

- [Issues](https://github.com/yunwei37/Prompt-Engineering-Guide-zh-CN/issues)
- [Project website](https://github.com/dair-ai/Prompt-Engineering-Guide)
- [README](https://github.com/yunwei37/Prompt-Engineering-Guide-zh-CN/blob/main/README.md)
- [yunwei37/Prompt-Engineering-Guide-zh-CN on GitHub](https://github.com/yunwei37/Prompt-Engineering-Guide-zh-CN)

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Hysen Labs editorial analysis, written from the project's own repository and release notes. Cite the canonical page: https://hysenlabs.com/projects/yunwei37-prompt-engineering-guide-zh-cn