> [!NOTE] > 本文档由 WeHub 基于上游 README 翻译整理，属于社区翻译，非官方中文文档。 > [English](./README.en.md) · [原始项目](https://github.com/aishwaryanr/awesome-generative-ai-guide) · [上游 README](https://github.com/aishwaryanr/awesome-generative-ai-guide/blob/HEAD/README.md) > 原作者、版权与许可证归属以原始项目及本仓库 LICENSE 文件为准。 # :star: :bookmark: awesome-generative-ai-guide 生成式 AI（Generative AI）正在快速发展，本仓库是获取生成式 AI 研究动态、面试资料、Notebook 等资源的综合枢纽！ aishwaryanr%2Fawesome-generative-ai-guide | Trendshift 探索以下资源： 1. [每月最佳 GenAI 论文清单](https://github.com/aishwaryanr/awesome-generative-ai-guide?tab=readme-ov-file#star-best-genai-papers-list-january-2024) 2. [GenAI 面试资源](https://github.com/aishwaryanr/awesome-generative-ai-guide?tab=readme-ov-file#computer-interview-prep) 3. [Applied LLMs Mastery 2024（由 Aishwarya Naresh Reganti 创建）课程资料](https://github.com/aishwaryanr/awesome-generative-ai-guide?tab=readme-ov-file#ongoing-applied-llms-mastery-2024) 4. [Generative AI Genius 2024（由 Aishwarya Naresh Reganti 创建）课程资料](https://github.com/aishwaryanr/awesome-generative-ai-guide/blob/main/free_courses/generative_ai_genius/README.md) 5. [AI Evals for Everyone（由 Aishwarya Naresh Reganti & Kiriti Badam 创建）- 获取认证！](https://github.com/aishwaryanr/awesome-generative-ai-guide/blob/main/free_courses/ai_evals_for_everyone/README.md) 6. **[全新] [OpenClaw Mastery for Everyone（由 Aishwarya Reganti & Kiriti Badam 创建）- 获取认证！](https://github.com/aishwaryanr/awesome-generative-ai-guide/blob/main/free_courses/openclaw_mastery_for_everyone/README.md)** 7. [全部 GenAI 相关免费课程清单（收录 90+ 门）](https://github.com/aishwaryanr/awesome-generative-ai-guide?tab=readme-ov-file#book-list-of-free-genai-courses) 8. [用于开发生成式 AI 应用的代码仓库/Notebook 清单](https://github.com/aishwaryanr/awesome-generative-ai-guide?tab=readme-ov-file#notebook-code-notebooks) 我们会定期更新本仓库，敬请关注最新内容！ 祝学习愉快！ --- ## :star: 顶级 AI 工具清单 探索我们精选的 AI 工具，覆盖 AI 应用开发的各个层面。点击[此处](https://github.com/aishwaryanr/awesome-generative-ai-guide/blob/main/resources/our_favourite_ai_tools.md)了解更多。 --- ## :speaker: 公告 - **全新：OpenClaw Mastery for Everyone 现已上线，支持认证！**（[点此查看](https://github.com/aishwaryanr/awesome-generative-ai-guide/blob/main/free_courses/openclaw_mastery_for_everyone/README.md)) - AI Evals for Everyone 课程现已上线，支持认证！（[点此查看](https://github.com/aishwaryanr/awesome-generative-ai-guide/blob/main/free_courses/ai_evals_for_everyone/README.md)) - Applied LLMs Mastery 完整课程内容已发布！！！（[点此查看](https://github.com/aishwaryanr/awesome-generative-ai-guide/blob/main/free_courses/Applied_LLMs_Mastery_2024)) - 5 天 LLM 基础学习路线图现已发布！（[点此查看](https://github.com/aishwaryanr/awesome-generative-ai-guide/blob/main/resources/genai_roadmap.md)) - 60 道常见 GenAI 面试题现已发布！（[点此查看](https://github.com/aishwaryanr/awesome-generative-ai-guide/blob/main/interview_prep/60_gen_ai_questions.md)) - ICLR 2024 论文摘要（[点此查看](https://areganti.notion.site/06f0d4fe46a94d62bff2ae001cfec22c?v=d501ca62e4b745768385d698f173ae14)) - 免费 GenAI 课程清单（[点此查看](https://github.com/aishwaryanr/awesome-generative-ai-guide#book-list-of-free-genai-courses)) - 生成式 AI 资源与学习路线图 - [3 天 RAG 学习路线图](https://github.com/aishwaryanr/awesome-generative-ai-guide/blob/main/resources/RAG_roadmap.md) - [5 天 LLM 基础学习路线图](https://github.com/aishwaryanr/awesome-generative-ai-guide/blob/main/resources/genai_roadmap.md) - [5 天 LLM Agent 学习路线图](https://github.com/aishwaryanr/awesome-generative-ai-guide/blob/main/resources/agents_roadmap.md) - [Agents 101 指南](https://github.com/aishwaryanr/awesome-generative-ai-guide/blob/main/resources/agents_101_guide.md) - [多模态 LLM（MM LLMs）入门](https://github.com/aishwaryanr/awesome-generative-ai-guide/blob/main/resources/mm_llms_guide.md) - [LLM Lingo 系列：常用 LLM 术语及其通俗定义](https://github.com/aishwaryanr/awesome-generative-ai-guide/blob/main/resources/llm_lingo) --- ## :mortar_board: 课程 #### [进行中] Applied LLMs Mastery 2024 加入 1000+ 名学员，开启为期 10 周的探索之旅，深入 LLM 在各类用例中的应用 #### [链接](https://areganti.notion.site/Applied-LLMs-Mastery-2024-562ddaa27791463e9a1286199325045c)至课程网站 ##### [2024 年 2 月] 报名仍在进行中，[点此报名](https://forms.gle/353sQMRvS951jDYu7) 🗓️\*Week 1 [Jan 15 2024]**\*: [LLM 实践入门](https://github.com/aishwaryanr/awesome-generative-ai-guide/blob/main/free_courses/Applied_LLMs_Mastery_2024/week1_part1_foundations.md)** - 应用型 LLM 基础 - 真实世界 LLM 用例 - 领域与任务适配方法 🗓️\*Week 2 [Jan 22 2024]**\*: [提示词（Prompting）与提示工程（Prompt Engineering）](https://github.com/aishwaryanr/awesome-generative-ai-guide/blob/main/free_courses/Applied_LLMs_Mastery_2024/week2_prompting.md)** - 提示词基本原则 - 提示词类型 - 应用、风险与高级提示技巧 🗓️\*Week 3 [Jan 29 2024]**\*: [LLM 微调（Fine-tuning）](https://github.com/aishwaryanr/awesome-generative-ai-guide/blob/main/free_courses/Applied_LLMs_Mastery_2024/week3_finetuning_llms.md)** - 微调基础 - 微调类型 - 微调挑战 🗓️\*Week 4 [Feb 5 2024]**\*: [RAG（检索增强生成，Retrieval-Augmented Generation）](https://github.com/aishwaryanr/awesome-generative-ai-guide/blob/main/free_courses/Applied_LLMs_Mastery_2024/week4_RAG.md)** - 理解 LLM 中的 RAG 概念 - RAG 核心组件 - 高级 RAG 方法 🗓️\*Week 5 [ Feb 12 2024]**\*: [构建 LLM 应用的工具](https://github.com/aishwaryanr/awesome-generative-ai-guide/blob/main/free_courses/Applied_LLMs_Mastery_2024/week5_tools_for_LLM_apps.md)** - 微调工具 - RAG 工具 - 可观测性、提示、Serving、向量检索等工具 🗓️\*Week 6 [Feb 19 2024]**\*: [评估技术](https://github.com/aishwaryanr/awesome-generative-ai-guide/blob/main/free_courses/Applied_LLMs_Mastery_2024/week6_llm_evaluation.md)** - 评估类型 - 常用评估基准 - 常用指标 🗓️\*Week 7 [Feb 26 2024]**\*: [构建你自己的 LLM 应用](https://github.com/aishwaryanr/awesome-generative-ai-guide/blob/main/free_courses/Applied_LLMs_Mastery_2024/week7_build_llm_app.md)** - LLM 应用组件 - 端到端构建你自己的 LLM 应用 🗓️\*Week 8 [March 4 2024]**\*: [高级功能与部署](https://github.com/aishwaryanr/awesome-generative-ai-guide/blob/main/free_courses/Applied_LLMs_Mastery_2024/week8_advanced_features.md)** - LLM 生命周期与 LLMOps - LLM 监控与可观测性 - 部署策略 🗓️\*Week 9 [March 11 2024]**\*: [LLM 相关挑战](https://github.com/aishwaryanr/awesome-generative-ai-guide/blob/main/free_courses/Applied_LLMs_Mastery_2024/week9_challenges_with_llms.md)** - 扩展性挑战 - 行为层面挑战 - 未来方向 🗓️\*Week 10 [March 18 2024]**\*: [新兴研究趋势](https://github.com/aishwaryanr/awesome-generative-ai-guide/blob/main/free_courses/Applied_LLMs_Mastery_2024/week10_research_trends.md)** - 更小且性能更强的模型 - 多模态模型 - LLM 对齐（Alignment） 🗓️*Week 11 *Bonus\* [March 25 2024]**\*: [基础](https://github.com/aishwaryanr/awesome-generative-ai-guide/blob/main/free_courses/Applied_LLMs_Mastery_2024/week11_foundations.md)** - 生成模型基础 - 自注意力与 Transformer - 语言神经网络 --- #### :book: 免费 GenAI 课程清单 ##### LLM 基础与入门 1. [大语言模型（Large Language Models）](https://rycolab.io/classes/llm-s23/) by ETH Zurich 2. [理解大语言模型（Understanding Large Language Models）](https://www.cs.princeton.edu/courses/archive/fall22/cos597G/) by Princeton 3. [Transformers 课程](https://huggingface.co/learn/nlp-course/chapter1/1) by Huggingface 4. [NLP 课程](https://huggingface.co/learn/nlp-course/chapter1/1) by Huggingface 5. [CS324 - 大语言模型](https://stanford-cs324.github.io/winter2022/) by Stanford 6. [大语言模型生成式 AI（Generative AI with Large Language Models）](https://www.coursera.org/learn/generative-ai-with-llms) by Coursera 7. [生成式 AI 入门（Introduction to Generative AI）](https://www.coursera.org/learn/introduction-to-generative-ai) by Coursera 8. [生成式 AI 基础（Generative AI Fundamentals）](https://www.cloudskillsboost.google/paths/118/course_templates/556) by Google Cloud 9. [5 日生成式 AI 强化课程（5-Day Gen AI Intensive Course）](https://www.youtube.com/watch?v=kpRyiJUUFxY&list=PLqFaTIg4myu-b1PlxitQdY0UYIbys-2es) by Google & Kaggle 10. [大语言模型入门（Introduction to Large Language Models）](https://www.cloudskillsboost.google/paths/118/course_templates/539) by Google Cloud 11. [生成式 AI 入门（Introduction to Generative AI）](https://www.cloudskillsboost.google/paths/118/course_templates/536) by Google Cloud 12. [生成式 AI 概念（Generative AI Concepts）](https://www.datacamp.com/courses/generative-ai-concepts) by DataCamp (Daniel Tedesco Data Lead @ Google) 13. [1 小时大语言模型（LLM）入门](https://www.youtube.com/watch?v=xu5_kka-suc) by WeCloudData 14. [从零开始的 LLM 基础模型 | 入门（LLM Foundation Models from the Ground Up | Primer）](https://www.youtube.com/watch?v=W0c7jQezTDw&list=PLTPXxbhUt-YWjMCDahwdVye8HW69p5NYS) by Databricks 15. [生成式 AI 详解（Generative AI Explained）](https://courses.nvidia.com/courses/course-v1:DLI+S-FX-07+V1/) by Nvidia 16. [Transformer 模型与 BERT 模型](https://www.cloudskillsboost.google/course_templates/538) by Google Cloud 17. [面向决策者的生成式 AI 学习计划（Generative AI Learning Plan for Decision Makers）](https://explore.skillbuilder.aws/learn/public/learning_plan/view/1909/generative-ai-learning-plan-for-decision-makers) by AWS 18. [负责任 AI 入门（Introduction to Responsible AI）](https://www.cloudskillsboost.google/course_templates/554) by Google Cloud 19. [生成式 AI 基础（Fundamentals of Generative AI）](https://learn.microsoft.com/en-us/training/modules/fundamentals-generative-ai/) by Microsoft Azure 20. [生成式 AI 初学者指南（Generative AI for Beginners）](https://github.com/microsoft/generative-ai-for-beginners?WT.mc_id=academic-122979-leestott) by Microsoft 21. [ChatGPT 初学者指南：人人都能用的终极用例（ChatGPT for Beginners: The Ultimate Use Cases for Everyone）](https://www.udemy.com/course/chatgpt-for-beginners-the-ultimate-use-cases-for-everyone/) by Udemy 22. [[1 小时讲座] 大语言模型入门（[1hr Talk] Intro to Large Language Models）](https://www.youtube.com/watch?v=zjkBMFhNj_g) by Andrej Karpathy 23. [面向所有人的 ChatGPT（ChatGPT for Everyone）](https://learnprompting.org/courses/chatgpt-for-everyone) by Learn Prompting 24. [大语言模型（LLMs）（英语）(Large Language Models (LLMs) (In English))](https://www.youtube.com/playlist?list=PLxlkzujLkmQ9vMaqfvqyfvZV_o8EqjAk7) by Kshitiz Verma (JK Lakshmipat University, Jaipur, India) 25. [生成式 AI 初学者指南（Generative AI for Beginners）](https://codekidz.ai/lesson-intro/generative-a-362093) By CodeKidz，基于 Microsoft 开源课程。 ##### 构建 LLM 应用（Building LLM Applications） 1. [LLMOps：用大语言模型构建真实世界应用（LLMOps: Building Real-World Applications With Large Language Models）](https://www.udacity.com/course/building-real-world-applications-with-large-language-models--cd13455) by Udacity 2. [全栈 LLM 训练营（Full Stack LLM Bootcamp）](https://fullstackdeeplearning.com/llm-bootcamp/) by FSDL 3. [生成式 AI 初学者指南（Generative AI for beginners）](https://github.com/microsoft/generative-ai-for-beginners/tree/main) by Microsoft 4. [大语言模型：从应用到生产（Large Language Models: Application through Production）](https://www.edx.org/learn/computer-science/databricks-large-language-models-application-through-production) by Databricks 5. [生成式 AI 基础（Generative AI Foundations）](https://www.youtube.com/watch?v=oYm66fHqHUM&list=PLhr1KZpdzukf-xb0lmiU3G89GJXaDbAIF) by AWS 6. [生成式 AI 社区课程入门（Introduction to Generative AI Community Course）](https://www.youtube.com/watch?v=ajWheP8ZD70&list=PLmQAMKHKeLZ-iTT-E2kK9uePrJ1Xua9VL) by ineuron 7. [LLM University](https://docs.cohere.com/docs/llmu) by Cohere 8. [LLM Learning Lab](https://lightning.ai/pages/llm-learning-lab/) by Lightning AI 9. [面向 LLM 应用开发的 LangChain（LangChain for LLM Application Development）](https://learn.deeplearning.ai/login?redirect_course=langchain&callbackUrl=https%3A%2F%2Flearn.deeplearning.ai%2Fcourses%2Flangchain) by Deeplearning.AI 10. [LLMOps](https://learn.deeplearning.ai/llmops) by DeepLearning.AI 11. [LLMOps 自动化测试（Automated Testing for LLMOps）](https://learn.deeplearning.ai/automated-testing-llmops) by DeepLearning.AI 12. [使用 Amazon Bedrock 构建生成式 AI 应用（Building Generative AI Applications Using Amazon Bedrock）](https://explore.skillbuilder.aws/learn/course/external/view/elearning/17904/building-generative-ai-applications-using-amazon-bedrock-aws-digital-training) by AWS 13. [高效部署 LLM（Efficiently Serving LLMs）](https://learn.deeplearning.ai/courses/efficiently-serving-llms/lesson/1/introduction) by DeepLearning.AI 14. [使用 ChatGPT API 构建系统（Building Systems with the ChatGPT API）](https://www.deeplearning.ai/short-courses/building-systems-with-chatgpt/) by DeepLearning.AI 15. [基于 Amazon Bedrock 的无服务器 LLM 应用（Serverless LLM apps with Amazon Bedrock）](https://www.deeplearning.ai/short-courses/serverless-llm-apps-amazon-bedrock/) by DeepLearning.AI 16. [使用向量数据库构建应用（Building Applications with Vector Databases）](https://www.deeplearning.ai/short-courses/building-applications-vector-databases/) by DeepLearning.AI 17. [LLMOps 自动化测试（Automated Testing for LLMOps）](https://www.deeplearning.ai/short-courses/automated-testing-llmops/) by DeepLearning.AI 18. [使用 LangChain.js 构建 LLM 应用（Build LLM Apps with LangChain.js）](https://www.deeplearning.ai/short-courses/build-llm-apps-with-langchain-js/) by DeepLearning.AI 19. [使用 Chroma 的高级检索（Advanced Retrieval for AI with Chroma）](https://www.deeplearning.ai/short-courses/advanced-retrieval-for-ai/) by DeepLearning.AI 20. [在 Azure 上运营 LLM（Operationalizing LLMs on Azure）](https://www.coursera.org/learn/llmops-azure) by Coursera 21. [生成式 AI 完整课程 – Gemini Pro、OpenAI、Llama、Langchain、Pinecone、向量数据库等（Generative AI Full Course – Gemini Pro, OpenAI, Llama, Langchain, Pinecone, Vector Databases & More）](https://www.youtube.com/watch?v=mEsleV16qdo) by freeCodeCamp.org 22. [面向生产的 LLM 训练与微调（Training & Fine-Tuning LLMs for Production）](https://learn.activeloop.ai/courses/llms) by Activeloop ##### 提示工程、RAG 与微调（Prompt Engineering, RAG and Fine-Tuning） 1. [生产环境中的 LangChain 与向量数据库（LangChain & Vector Databases in Production）](https://www.youtube.com/redirect?event=video_description&redir_token=QUFFLUhqbVhnQW8xNDdhSU9IUDVLXzFhV2N0UkNRMkZrQXxBQ3Jtc0traUxHMzZJcGJQYjlyckYxaGxYVWlsOFNGUFlFVEdhNzdjTWpPUlQ2TF9XczRqNkxMVGpJTnd5YmYzV0prQ0IwZURNcHhIZ3h1Z051VTl5MXBBLUN0dkM0NHRkQTFua1Jpc0VCRFJUb0ZQZG95b0JqMA&q=https%3A%2F%2Flearn.activeloop.ai%2Fcourses%2Flangchain&v=gKUTDC13jys) by Activeloop 2. [基于人类反馈的强化学习（Reinforcement Learning from Human Feedback）](https://learn.deeplearning.ai/reinforcement-learning-from-human-feedback) by DeepLearning.AI 3. [使用向量数据库构建应用（Building Applications with Vector Databases）](https://learn.deeplearning.ai/building-applications-vector-databases) by DeepLearning.AI 4. [大语言模型微调（Finetuning Large Language Models）](https://learn.deeplearning.ai/finetuning-large-language-models) by Deeplearning.AI 5. [LangChain：与你的数据对话（LangChain: Chat with Your Data）](https://learn.deeplearning.ai/langchain-chat-with-your-data/) by Deeplearning.AI 6. [使用 ChatGPT API 构建系统（Building Systems with the ChatGPT API）](https://learn.deeplearning.ai/chatgpt-building-system) by Deeplearning.AI 7. [Llama 2 提示工程（Prompt Engineering with Llama 2）](https://www.deeplearning.ai/short-courses/prompt-engineering-with-llama-2/) by Deeplearning.AI 8. [使用向量数据库构建应用（Building Applications with Vector Databases）](https://learn.deeplearning.ai/building-applications-vector-databases) by Deeplearning.AI 9. [面向开发者的 ChatGPT 提示工程（ChatGPT Prompt Engineering for Developers）](https://learn.deeplearning.ai/chatgpt-prompt-eng/lesson/1/introduction) by Deeplearning.AI 10. [高级 RAG 编排系列（Advanced RAG Orchestration series）](https://www.youtube.com/watch?v=CeDS1yvw9E4) by LlamaIndex 11. [提示工程专项课程（Prompt Engineering Specialization）](https://www.coursera.org/specializations/prompt-engineering) by Coursera 12. [使用检索增强生成（RAG）增强你的 LLM（Augment your LLM Using Retrieval Augmented Generation）](https://courses.nvidia.com/courses/course-v1:NVIDIA+S-FX-16+v1/) by Nvidia 13. [面向 RAG 的知识图谱（Knowledge Graphs for RAG）](https://www.deeplearning.ai/short-courses/knowledge-graphs-rag/) by Deeplearning.AI 14. [Hugging Face 开源模型（Open Source Models with Hugging Face）](https://www.deeplearning.ai/short-courses/open-source-models-hugging-face/) by Deeplearning.AI 15. [向量数据库：从嵌入到应用（Vector Databases: from Embeddings to Applications）](https://www.deeplearning.ai/short-courses/vector-databases-embeddings-applications/) by Deeplearning.AI 16. [理解与应用文本嵌入（Understanding and Applying Text Embeddings）](https://www.deeplearning.ai/short-courses/google-cloud-vertex-ai/) by Deeplearning.AI 17. [使用 LlamaIndex 的 JavaScript RAG Web 应用（JavaScript RAG Web Apps with LlamaIndex）](https://www.deeplearning.ai/short-courses/javascript-rag-web-apps-with-llamaindex/) by Deeplearning.AI 18. [Hugging Face 量化基础（Quantization Fundamentals with Hugging Face）](https://www.deeplearning.ai/short-courses/quantization-fundamentals-with-hugging-face/) by Deeplearning.AI 19. [为 LLM 应用预处理非结构化数据（Preprocessing Unstructured Data for LLM Applications）](https://www.deeplearning.ai/short-courses/preprocessing-unstructured-data-for-llm-applications/) by Deeplearning.AI 20. [使用 LangChain 与 LlamaIndex 实现生产级检索增强生成（Retrieval Augmented Generation for Production with LangChain & LlamaIndex）](https://learn.activeloop.ai/courses/rag) by Activeloop 21. [量化深入讲解（Quantization in Depth）](https://www.deeplearning.ai/short-courses/quantization-in-depth/) by Deeplearning.AI ##### 评估 1. [构建与评估高级 RAG 应用](https://learn.deeplearning.ai/building-evaluating-advanced-rag) by DeepLearning.AI 2. [使用 Weights and Biases 评估与调试生成式 AI 模型](https://learn.deeplearning.ai/evaluating-debugging-generative-ai) by Deeplearning.AI 3. [LLM 应用的质量与安全](https://www.deeplearning.ai/short-courses/quality-safety-llm-applications/) by Deeplearning.AI 4. [LLM 应用的红队测试](https://www.deeplearning.ai/short-courses/red-teaming-llm-applications/?utm_campaign=giskard-launch&utm_medium=headband&utm_source=dlai-homepage) by Deeplearning.AI ##### 多模态 1. [扩散模型原理](https://www.deeplearning.ai/short-courses/how-diffusion-models-work/) by DeepLearning.AI 2. [如何使用 Midjourney、AI Art 和 ChatGPT 打造精彩网站](https://www.youtube.com/watch?v=5wdCev86RYE) by Brad Hussey 3. [使用 ChatGPT、DALL-E 和 GPT-4 构建 AI 应用](https://scrimba.com/learn/buildaiapps) by Scrimba 4. [11-777：多模态机器学习](https://www.youtube.com/playlist?list=PL-Fhd_vrvisNM7pbbevXKAbT_Xmub37fA) by Carnegie Mellon University 5. [视觉模型的提示工程](https://www.deeplearning.ai/short-courses/prompt-engineering-for-vision-models/) by Deeplearning.AI ##### 智能体 1. [使用 LLM 构建 RAG 智能体](https://courses.nvidia.com/courses/course-v1:DLI+S-FX-15+V1/) by Nvidia 2. [LangChain 中的函数、工具与智能体](https://learn.deeplearning.ai/functions-tools-agents-langchain) by Deeplearning.AI 3. [LangGraph 中的 AI 智能体](https://www.deeplearning.ai/short-courses/ai-agents-in-langgraph/) by Deeplearning.AI 4. [AutoGen 的 AI 智能体设计模式](https://www.deeplearning.ai/short-courses/ai-agentic-design-patterns-with-autogen/) by Deeplearning.AI 5. [使用 crewAI 构建多 AI 智能体系统](https://www.deeplearning.ai/short-courses/multi-ai-agent-systems-with-crewai/) by Deeplearning.AI 6. [使用 LlamaIndex 构建智能体式 RAG](https://www.deeplearning.ai/short-courses/building-agentic-rag-with-llamaindex/) by Deeplearning.AI 7. [LLM 可观测性：智能体、工具与链](https://courses.arize.com/p/agents-tools-and-chains) by Arize AI 8. [使用 LlamaIndex 构建智能体式 RAG](https://www.deeplearning.ai/short-courses/building-agentic-rag-with-llamaindex/) by Deeplearning.AI 9. [Amazon Bedrock 智能体工具与函数调用（操作指南）](https://www.youtube.com/watch?app=desktop&v=2L_XE6g3atI) by AWS Developers 10. [ChatGPT 与 Zapier：面向所有人的智能体式 AI](https://www.coursera.org/learn/agentic-ai-chatgpt-zapier) by Coursera 11. [使用 AutoGen 的多智能体系统](https://www.manning.com/books/multi-agent-systems-with-autogen-cx) by Victor Dibia [Book] 12. [大型语言模型智能体 MOOC，2024 年秋季](https://llmagents-learning.org/f24) by Dawn Song & Xinyun Chen – 一门涵盖 LLM 智能体基础与高级主题的综合课程。 13. [CS294/194-196 大型语言模型智能体](https://rdi.berkeley.edu/llm-agents/f24) by UC Berkeley #### 其他 1. [避免 AI 危害](https://www.coursera.org/learn/avoiding-ai-harm) by Coursera 2. [制定 AI 政策](https://www.coursera.org/learn/developing-ai-policy) by Coursera --- ## :paperclip: 资源 - [ICLR 2024 论文摘要](https://areganti.notion.site/06f0d4fe46a94d62bff2ae001cfec22c?v=d501ca62e4b745768385d698f173ae14) --- ## :computer: 面试准备 #### 按主题分类的问题： 1. [常见 GenAI 面试题](https://github.com/aishwaryanr/awesome-generative-ai-guide/blob/main/interview_prep/60_gen_ai_questions.md) 2. 提示词与提示工程 3. 模型微调 4. 模型评估 5. 生成式 AI 的 MLOps 6. 生成模型基础 7. 最新研究趋势 #### 生成式 AI 系统设计（即将推出）： 1. 设计基于 LLM 的搜索引擎 2. 构建客户支持聊天机器人 3. 构建用于与你的数据进行自然语言交互的系统。 4. 构建 AI 副驾驶 5. 为多模态数据（文本、图像、表格、CSV 文件）设计自定义问答聊天机器人 6. 构建电商自动化商品描述与图像生成系统 --- ## :notebook: 代码笔记本 #### RAG 教程 - [AWS Bedrock 工作坊教程](https://github.com/aws-samples/amazon-bedrock-workshop) by Amazon Web Services - [Langchain 教程](https://github.com/gkamradt/langchain-tutorials) by gkamradt - [生产环境 LLM 应用](https://github.com/ray-project/llm-applications/tree/main) by ray-project - [LLM 教程](https://github.com/ollama/ollama/tree/main/examples) by Ollama - [LLM Hub](https://github.com/mallahyari/llm-hub) by mallahyari - [RAG cookbook](https://docs.camel-ai.org/cookbooks/agents_with_rag.html) by CAMEL-AI #### 微调教程 - [LLM 微调教程](https://github.com/ashishpatel26/LLM-Finetuning) by ashishpatel26 - [PEFT](https://github.com/huggingface/peft/tree/main/examples) example notebooks by Huggingface - [免费 LLM 微调 Notebooks](https://levelup.gitconnected.com/14-free-large-language-models-fine-tuning-notebooks-532055717cb7) by Youssef Hosni #### 综合 LLM 代码仓库 - [LLM-PlayLab](https://github.com/Sakil786/LLM-PlayLab) 该 PlayLab 汇集了众多基于 Transformer 模型构建的项目 - [RAG Techniques](https://github.com/NirDiamant/RAG_Techniques) by Nir Diamant — 35+ 个可运行的 Jupyter notebook，涵盖高级 RAG 技术（分块、查询转换/HyDE、重排序、self-RAG、图 RAG、评估） - [GenAI Agents](https://github.com/NirDiamant/GenAI_Agents) by Nir Diamant — 50+ 篇教程与参考实现，涵盖从简单机器人到多智能体系统的 GenAI 智能体构建 --- ## :black_nib: 贡献 如果你想为仓库做贡献或发现任何问题，欢迎提交 PR，并确保内容放在正确的章节或分类中。 --- ## :pushpin: 引用我们 如需引用本指南，请使用以下格式： ``` @article{areganti_generative_ai_guide, author = {Reganti, Aishwarya Naresh}, journal = {https://github.com/aishwaryanr/awesome-generative-ai-resources}, month = {01}, title = {{Generative AI Guide}}, year = {2024} } ``` ## 许可证 [MIT License] ** 本节内容由赞助商提供。我们不对该产品/服务作背书或担保，对其使用所产生的任何问题不承担责任。请自行评估并酌情使用。 ## 安全与防护工具 - **[OWASP Agent Memory Guard](https://github.com/OWASP/www-project-agent-memory-guard)** - OWASP 官方参考实现，用于防御 AI 智能体记忆投毒（来自《OWASP 智能体 AI 系统十大风险》的 ASI06）。为智能体记忆提供写入前扫描、读取前验证与审计日志。