# AIGC-Interview-Book: A Chinese-Language Interview Prep Repository for AIGC, LLM and AI Agent Roles

> WeThinkIn/AIGC-Interview-Book is a GPL-3.0 licensed collection of Markdown study tracks and question banks for AI algorithm and engineering interviews, with a companion site and a paid community attached. It is a reading resource, not software you run.

**WeThinkIn/AIGC-Interview-Book** — 【三年面试五年模拟】AIGC/LLM/AI Agent算法工程师面试资源平台。涵盖AIGC、LLM大模型、AI Agent、具身智能、传统深度学习、计算机视觉、自然语言处理、自动驾驶、机器学习、强化学习、大数据挖掘、世界模型、元宇宙、AGI等AI行业面试笔试干货经验与核心跨周期知识。

- Repository: https://github.com/WeThinkIn/AIGC-Interview-Book
- Website: https://wethinkin.github.io/AIGC-Interview-Book/
- Stars: 4,853 · Forks: 471
- Language: Unknown
- License: GPL-3.0
- Published: 2026-09-10 · Updated: 2026-09-10 · Language: en
- Canonical page: https://hysenlabs.com/projects/wethinkin-aigc-interview-book

## What AIGC-Interview-Book Actually Is

This is a documentation repository, not a library. There is no package to import, no server to start, no CLI. The README describes it as a platform for AIGC, LLM and AI Agent interview preparation, and the top-level directory listing backs that up: folders such as 大模型基础, AI Agent基础, 模型部署基础, 深度学习基础, 机器学习基础, 编程基础：Python and 大厂高频面试题（实时更新） hold Markdown files. A docs/ directory and a workspace/ directory sit alongside them, and the project publishes a GitHub Pages site at wethinkin.github.io/AIGC-Interview-Book.

The audience is narrow and clearly stated. The README says the material suits AIGC, LLM and AI Agent job seekers as well as the interviewers who hire them, and that it can serve as a reference source for university research and teaching in those areas. It also lists three tracks: algorithm roles, development roles (FDE, Python, Java, C/C++, Go, embedded, frontend, backend, testing, operations) and application roles (product, operations, design, business, marketing). The algorithm track is where the bulk of the directory structure points.

The framing is opinionated in a way that matters for how you read it. The README states that not every AI technology has cross-cycle value, and that content enters the platform only after what it calls sustained evaluation across industry, academia, competitions, investment and application. That is a curation claim, not a verifiable one. What you can verify is the folder layout, the file names, and whether the answers are as deep as the question titles suggest.

## How the Material Is Organised and Where the Content Comes From

The repository is a tree of topic folders, and the README's table of contents mirrors that tree almost one to one. 大模型基础（精华版）, 深度学习基础（精华版）, AI多模态基础（精华版）, AI视频基础（精华版） and 模型部署基础（精华版） appear as condensed variants of their parent folders, so the same subject exists at two levels of detail. That duplication is a deliberate editorial choice, not an accident, and it means you should check the 精华版 file first if you only want the compressed version.

Alongside the topic folders there is 热门AI学习核心课程与教程, which holds fifteen numbered Markdown files. The README names them: 2026 AI Agent 岗面试 50 问, 多模态算法岗 30 天复习路线, 大模型面试最高频 100 问, AI 算法岗薪资与岗位技术栈地图, 从传统 CV 转 AIGC 的最短路径, 30 天 AI 算法岗冲刺路线, 60 天大模型算法岗系统路线, AI Agent 工程岗面试路线, 多模态算法岗面试路线, AIGC 图像/视频生成算法岗路线, 模型部署与推理优化路线, 传统 CV/NLP 转大模型路线, 开发岗转 AI 应用工程师路线, 校招 AI 算法岗求职路线 and 社招 AI 算法岗跳槽路线. Several are study schedules with a fixed day count, which makes them the most actionable entries in the tree.

The provenance claim in the README is that the core content is distilled from the editors' work, research and competition experience, and that it includes written and interview questions from large internet companies and AI companies. The named editors are Rocky Ding (主编), 猫先生, 张一凡, 徐晨轩 and 刘一手 (副主编). Rocky Ding's biography in the README cites experience across internet companies, AI unicorns, traditional tech firms and a state-owned research institute, plus competition results and a Zhihu following. None of that is independently checkable from the repository, and the README does not cite sources per question, so treat the question banks as curated study prompts rather than as official company interview records.

## Reading Your First File From the Public Tree

There is no installer and no dependency file, because the deliverable is Markdown. The README does not give a git command, so the only documented entry points are the GitHub Pages site and the repository itself. The README's own table of contents is the fastest index, and it links directly to the folders you need, including 大模型基础（精华版）, AI Agent基础, 模型部署基础 and 大厂高频面试题（实时更新）.

The README's table of contents is the fastest index. To jump straight to a study plan rather than a topic folder, start with the numbered files it links under 热门AI学习核心课程与教程:

```text
热门AI学习核心课程与教程/01_2026_AI_Agent岗面试50问.md
热门AI学习核心课程与教程/02_多模态算法岗30天复习路线.md
热门AI学习核心课程与教程/03_大模型面试最高频100问.md
```

Those three paths are the ones the README's tutorial table exposes, and they map to the three questions it names in the same table: Agent 本质、工具协议、Memory、评测与平台设计 for the first, 视觉语言模型、模态对齐、训练微调与面试表达 for the second, and 架构、训练、后训练、RAG、推理与评测 for the third. Read the 100-question file first if your target role is a general LLM algorithm position. For an Agent engineering role, the README points to AI Agent 工程岗面试路线, which it says covers workflows, tools, MCP, Memory, AgentOps and system design. There is nothing to build and nothing to run, so the first real use is reading and taking notes.

## The Paid Community Behind the Public Repository

This is the part most readers will want to understand before committing time. The README states that the project originates from the AIGCmagic community and that a paid advanced community offers question answering, referrals, resume review, mock interviews, project coaching and career planning. Entry is described as a WeChat QR code scan, with an image at imgs/星球优惠券.png, and the README mentions a VIP group contact handle, Jarvis8866, with a required note format of community nickname, city, direction and company or school.

So the public repository is a funnel as well as a resource. That does not make the free content thin: the directory listing shows substantial topic coverage, and the fifteen numbered course files are in the public tree. But the README explicitly says the specifics of the paid services, their availability and their scope follow whatever the community publishes, which means the repository cannot tell you what you get for the money. If you are evaluating this for a team or a course, budget for the possibility that the most detailed answers live behind that wall.

The README also asks readers to star the project, with a joking line about stars translating into offers. Star counts are not evidence of quality, and the repository does not present them as such; the claim is marketing copy, not a technical statement.

## Where This Repository Falls Short

The first limitation is language. Every folder name, file name and question in the repository is in Chinese. If your interview loop is in English, or your target employer does not interview in Chinese, the question banks will still be useful for topic coverage but useless for practising phrasing. There is no English edition documented in the README.

The second is that it is not a runnable artefact. There is no code to execute, no test suite, no benchmark, and no dependency manifest. You cannot verify an answer by running it. For a repository about LLM and Agent topics, that is a real gap: questions about RAG pipelines, inference optimisation or Agent memory are far more convincing when paired with code you can inspect, and the README's description of the course files suggests prose and question lists rather than implementations.

The third is maintenance granularity. The last push to the default branch was on 2026-09-08, which is recent, but a push date tells you nothing about which files changed. One folder in the tree is literally named 大厂高频面试题（实时更新）, which promises real-time updates that a git history is the only way to confirm. If you plan to rely on a specific question file for a loop next month, check that file's commit history rather than the repository's overall activity.

Finally, the curation claim cuts both ways. The README says content is admitted only after sustained evaluation, which implies topics that were popular but judged not to have cross-cycle value were excluded. That is a defensible editorial stance, but it means the repository is a filtered view of the field, and you will not find coverage of whatever the maintainers consider transient. If your target role is built on something recent and fast-moving, the filter may work against you.

## How It Differs From Cracking the Coding Interview and From Question-Bank Sites

The obvious comparison, and one people search for directly, is Cracking the Coding Interview. The difference is not quality, it is domain and format. That book is a printed, English-language, algorithm-and-data-structure drill set with a stable edition and a fixed set of problems. This repository is a living Chinese-language tree aimed at AIGC, LLM, AI Agent, computer vision, NLP, autonomous driving, machine learning, reinforcement learning and deployment topics, with career material mixed into the technical folders. If your interview is a LeetCode-style loop, the book is the closer match; if it is a system-and-concepts loop about transformers, diffusion models, RAG or Agent design, this repository covers ground the book does not touch at all.

A second comparison is with question-bank websites and paid interview courses. Those typically gate content behind an account, version their questions per company, and offer search and filtering. This repository gives you plain Markdown in a git tree: you can fork it, diff it, grep it, and read it offline, which is a genuine advantage for anyone who wants the material in their own notes pipeline. The trade-off is the absence of structure. There is no schema, no tagging, no difficulty rating and no per-question source attribution, so building anything automated on top of it means parsing prose yourself.

## Licence and the Cost of Keeping It Current

The repository is licensed GPL-3.0, with the LICENSE file at the top level. That is a copyleft licence, which is unusual for a documentation project and worth pausing on. GPL-3.0 is written for software, and applying it to Markdown prose creates ambiguity about what counts as a derivative work. If you plan to reuse substantial portions in your own training material, a course, or an internal knowledge base, read the LICENSE text and, if the stakes are high, get your own legal advice. Nothing here should be read as legal advice, and the README does not state a separate content licence for the Markdown files.

Upgrade cost is low in the mechanical sense. There is no version to pin and no migration path; a git pull brings whatever the maintainers have written since your last fetch. The real cost is editorial. The repository is a curated set, and the README's own standard is that only cross-cycle knowledge is admitted, which means content can be reorganised or removed as the maintainers' judgement shifts. If you have built notes that reference specific file paths, a rename of a 精华版 folder or a renumbering in 热门AI学习核心课程与教程 will break those references. Forking the repository, or vendoring the files you depend on into your own repository, is the cheap insurance against that.

## Conclusion

Adopt it if you are preparing for a Chinese-market AIGC, LLM or AI Agent interview and want a structured reading list rather than a tool: open 热门AI学习核心课程与教程/03_大模型面试最高频100问.md and 01_2026_AI_Agent岗面试50问.md first, and judge the depth against your own target job description. Do not adopt it if you need English material, runnable code, or a machine-readable question bank to build a product on, and do not treat the GPL-3.0 licence as permission to reprint the Markdown inside a commercial course without reading the LICENSE file and the linked community terms. Verify two things before you invest weeks: whether the question files you care about have been touched since the last push on 2026-09-08, and whether the answers you need sit behind the paid AIGCmagic community rather than in the public tree.

## FAQ

### Is AIGC-Interview-Book still relevant for AI interviews in 2026?

The repository's most recent push to the default branch was on 2026-09-08, and its course list includes files dated to 2026 such as 2026 AI Agent 岗面试 50 问. Whether a specific question file is current depends on that file's own history, which the README does not summarise.

### How should I prepare for an interview using AIGC-Interview-Book?

The README points to 热门AI学习核心课程与教程, which holds numbered study routes including 30 天 AI 算法岗冲刺路线 and 60 天大模型算法岗系统路线, plus question files such as 大模型面试最高频 100 问. Start from the route that matches your target role and read the corresponding topic folder for depth.

### Is AIGC-Interview-Book worth the time compared with a paid interview course?

The public repository is free to clone and covers AIGC, LLM, AI Agent, computer vision, NLP, deployment and career topics in Markdown, but the README states that more detailed services such as mock interviews, resume review and project coaching live in the paid AIGCmagic community. Judge the public files first and treat the community as a separate decision.

### How do I crack an AI interview using this repository?

The repository is a reading resource with no code to run, so the practical path is to work through the study route for your target role and use the question files in 大厂高频面试题（实时更新） and 热门AI学习核心课程与教程 as prompts. The README also notes the material is aimed at interviewers as well as candidates.

## Sources

- [Issues](https://github.com/WeThinkIn/AIGC-Interview-Book/issues)
- [License: GPL-3.0](https://github.com/WeThinkIn/AIGC-Interview-Book/blob/main/LICENSE)
- [Project website](https://wethinkin.github.io/AIGC-Interview-Book/)
- [README](https://github.com/WeThinkIn/AIGC-Interview-Book/blob/main/README.md)
- [WeThinkIn/AIGC-Interview-Book on GitHub](https://github.com/WeThinkIn/AIGC-Interview-Book)

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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/wethinkin-aigc-interview-book