# 附录 C：参考资源

本附录汇集上下文工程领域的重要学习资源。

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## 官方文档

### OpenAI

* **文档首页**：[OpenAI Docs](https://platform.openai.com/docs)
* **提示词工程指南**：[OpenAI Prompt Engineering Guide](https://platform.openai.com/docs/guides/prompt-engineering)
* **函数调用**：[OpenAI Function Calling Guide](https://platform.openai.com/docs/guides/function-calling)

### Anthropic

* **Claude 文档**：[Anthropic Docs](https://docs.anthropic.com)
* **提示词库**：[Anthropic Prompt Library](https://docs.anthropic.com/en/prompt-library)
* **Claude 系统提示词指南**

### Google

* **Gemini API 文档**：[Google AI Docs](https://ai.google.dev/docs)
* **Vertex AI 文档**：[Vertex AI Docs](https://cloud.google.com/vertex-ai/docs)

***

## 技术博客

### 公司博客

* **OpenAI Blog**：[官方博客](https://openai.com/blog)
* **Anthropic Research**：[研究主页](https://www.anthropic.com/research)
* **Anthropic Engineering Blog**：[工程博客](https://www.anthropic.com/engineering) ⭐ *上下文工程核心参考*
* **Google AI Blog**：[官方博客](https://blog.research.google)

### 上下文工程专题文章

以下是对本书内容有重要参考价值的官方文章：

| 文章                                          | 来源        | 相关章节      |
| ------------------------------------------- | --------- | --------- |
| Effective context engineering for AI agents | Anthropic | 第 3、6、9 章 |
| Writing effective tools for agents          | Anthropic | 第 8 章     |
| Introducing advanced tool use               | Anthropic | 第 8 章     |
| Building effective agents                   | Anthropic | 第 9 章     |
| Multi-agent research system                 | Anthropic | 第 9 章     |

### 技术社区

* **Towards Data Science**：[主页](https://towardsdatascience.com)
* **Medium AI 专栏**
* **各框架官方博客**

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## 学术论文

### 核心论文

* **Attention Is All You Need** (2017) - Transformer 架构
* **Retrieval-Augmented Generation** (2020) - [RAG](/context_engineering_guide/di-er-bu-fen-he-xin-ji-shu-yu-ce-le/05_select/5.1_rag_principles.md) 原论文
* **Chain-of-Thought Prompting** (2022) - 思维链
* [**ReAct**](/context_engineering_guide/di-san-bu-fen-jin-jie-ji-shu-yu-jia-gou/09_agents/9.1_agent_architecture.md) (2022) - 推理与行动结合

### 推荐阅读平台

* **arXiv**：[主页](https://arxiv.org)
* **Papers with Code**：[主页](https://paperswithcode.com)
* **Semantic Scholar**：[主页](https://semanticscholar.org)

***

## 在线课程

### DeepLearning.AI

* **ChatGPT Prompt Engineering**
* **LangChain 系列课程**
* **Building Systems with the ChatGPT API**

### 其他平台

* **Coursera** LLM 相关课程
* **Udemy** 实战课程
* **YouTube** 技术频道

***

## 开源项目

### 学习参考

* **LangChain 示例**：[仓库](https://github.com/langchain-ai/langchain)
* **LlamaIndex 示例**：[仓库](https://github.com/run-llama/llama_index)
* **Awesome LLM**：[仓库](https://github.com/Hannibal046/Awesome-LLM)

### RAG 实现

* **RAGFlow**：开源 RAG 引擎
* **Verba**：开源检索助手
* **PrivateGPT**：本地 RAG 系统

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## 社区资源

### 即时通讯社区

* LangChain Discord
* Weaviate Slack
* LlamaIndex Discord

### 论坛

* Reddit r/LocalLLaMA
* Hacker News
* Stack Overflow

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## 书籍推荐

### 中文

* 本书《大模型上下文工程权威指南》

### 英文

* **Building LLM Apps** - O'Reilly
* **Prompt Engineering for Developers**

***

## 持续学习建议

1. **关注官方更新**：各模型厂商的最新文档和博客
2. **追踪论文**：关键会议如 NeurIPS、ICML、ACL
3. **参与社区**：加入相关 Discord/Slack
4. **动手实践**：通过项目积累经验
5. **分享交流**：输出倒逼输入

***

*本附录资源会随时间变化，建议定期检查最新版本。*


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