[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-73522":3},{"id":4,"name":5,"fullName":6,"owner":7,"repo":5,"description":8,"homepage":9,"htmlUrl":10,"language":11,"languages":10,"totalLinesOfCode":10,"stars":12,"forks":13,"watchers":14,"openIssues":15,"contributorsCount":16,"subscribersCount":16,"size":16,"stars1d":17,"stars7d":18,"stars30d":19,"stars90d":16,"forks30d":16,"starsTrendScore":20,"compositeScore":21,"rankGlobal":10,"rankLanguage":10,"license":22,"archived":23,"fork":23,"defaultBranch":24,"hasWiki":25,"hasPages":23,"topics":26,"createdAt":10,"pushedAt":10,"updatedAt":35,"readmeContent":36,"aiSummary":37,"trendingCount":16,"starSnapshotCount":16,"syncStatus":38,"lastSyncTime":39,"discoverSource":40},73522,"awesome-generative-ai-guide","aishwaryanr\u002Fawesome-generative-ai-guide","aishwaryanr","A one stop repository for generative AI research updates, interview resources, notebooks and much more!","https:\u002F\u002Fwww.linkedin.com\u002Fin\u002Fareganti\u002F",null,"HTML",27094,5687,566,5,0,58,158,446,174,45,"MIT License",false,"main",true,[27,28,29,30,31,32,33,34],"awesome","awesome-list","generative-ai","interview-questions","large-language-models","llms","notebook-jupyter","vision-and-language","2026-06-12 02:03:14","# :star: :bookmark: awesome-generative-ai-guide\n\nGenerative AI is experiencing rapid growth, and this repository serves as a comprehensive hub for updates on generative AI research, interview materials, notebooks, and more!\n\n\u003Ca href=\"https:\u002F\u002Ftrendshift.io\u002Frepositories\u002F7663\" target=\"_blank\">\u003Cimg src=\"https:\u002F\u002Ftrendshift.io\u002Fapi\u002Fbadge\u002Frepositories\u002F7663\" alt=\"aishwaryanr%2Fawesome-generative-ai-guide | Trendshift\" style=\"width: 250px; height: 55px;\" width=\"250\" height=\"55\"\u002F>\u003C\u002Fa>\n\nExplore the following resources:\n\n1. [Monthly Best GenAI Papers List](https:\u002F\u002Fgithub.com\u002Faishwaryanr\u002Fawesome-generative-ai-guide?tab=readme-ov-file#star-best-genai-papers-list-january-2024)\n2. [GenAI Interview Resources](https:\u002F\u002Fgithub.com\u002Faishwaryanr\u002Fawesome-generative-ai-guide?tab=readme-ov-file#computer-interview-prep)\n3. [Applied LLMs Mastery 2024 (created by Aishwarya Naresh Reganti) course material](https:\u002F\u002Fgithub.com\u002Faishwaryanr\u002Fawesome-generative-ai-guide?tab=readme-ov-file#ongoing-applied-llms-mastery-2024)\n4. [Generative AI Genius 2024 (created by Aishwarya Naresh Reganti) course material](https:\u002F\u002Fgithub.com\u002Faishwaryanr\u002Fawesome-generative-ai-guide\u002Fblob\u002Fmain\u002Ffree_courses\u002Fgenerative_ai_genius\u002FREADME.md)\n5. [AI Evals for Everyone (created by Aishwarya Naresh Reganti & Kiriti Badam) - Get Certified!](https:\u002F\u002Fgithub.com\u002Faishwaryanr\u002Fawesome-generative-ai-guide\u002Fblob\u002Fmain\u002Ffree_courses\u002Fai_evals_for_everyone\u002FREADME.md)\n6. **[NEW] [OpenClaw Mastery for Everyone (created by Aishwarya Reganti & Kiriti Badam) - Get Certified!](https:\u002F\u002Fgithub.com\u002Faishwaryanr\u002Fawesome-generative-ai-guide\u002Fblob\u002Fmain\u002Ffree_courses\u002Fopenclaw_mastery_for_everyone\u002FREADME.md)**\n7. [List of all GenAI-related free courses (over 90 listed)](https:\u002F\u002Fgithub.com\u002Faishwaryanr\u002Fawesome-generative-ai-guide?tab=readme-ov-file#book-list-of-free-genai-courses)\n8. [List of code repositories\u002Fnotebooks for developing generative AI applications](https:\u002F\u002Fgithub.com\u002Faishwaryanr\u002Fawesome-generative-ai-guide?tab=readme-ov-file#notebook-code-notebooks)\n\nWe'll be updating this repository regularly, so keep an eye out for the latest additions!\n\nHappy Learning!\n\n---\n## :star: Top AI Tools List\n\nDiscover our favorite AI tools spanning every layer of AI application development. Click [here](https:\u002F\u002Fgithub.com\u002Faishwaryanr\u002Fawesome-generative-ai-guide\u002Fblob\u002Fmain\u002Fresources\u002Four_favourite_ai_tools.md) to learn more.\n\n---\n\n## :speaker: Announcements\n\n- **NEW: OpenClaw Mastery for Everyone is now live with certification!** ([Click Here](https:\u002F\u002Fgithub.com\u002Faishwaryanr\u002Fawesome-generative-ai-guide\u002Fblob\u002Fmain\u002Ffree_courses\u002Fopenclaw_mastery_for_everyone\u002FREADME.md))\n- AI Evals for Everyone course is now live with certification! ([Click Here](https:\u002F\u002Fgithub.com\u002Faishwaryanr\u002Fawesome-generative-ai-guide\u002Fblob\u002Fmain\u002Ffree_courses\u002Fai_evals_for_everyone\u002FREADME.md))\n- Applied LLMs Mastery full course content has been released!!! ([Click Here](https:\u002F\u002Fgithub.com\u002Faishwaryanr\u002Fawesome-generative-ai-guide\u002Fblob\u002Fmain\u002Ffree_courses\u002FApplied_LLMs_Mastery_2024))\n- 5-day roadmap to learn LLM foundations out now! ([Click Here](https:\u002F\u002Fgithub.com\u002Faishwaryanr\u002Fawesome-generative-ai-guide\u002Fblob\u002Fmain\u002Fresources\u002Fgenai_roadmap.md))\n- 60 Common GenAI Interview Questions out now! ([Click Here](https:\u002F\u002Fgithub.com\u002Faishwaryanr\u002Fawesome-generative-ai-guide\u002Fblob\u002Fmain\u002Finterview_prep\u002F60_gen_ai_questions.md))\n- ICLR 2024 paper summaries ([Click Here](https:\u002F\u002Fareganti.notion.site\u002F06f0d4fe46a94d62bff2ae001cfec22c?v=d501ca62e4b745768385d698f173ae14))\n- List of free GenAI courses ([Click Here](https:\u002F\u002Fgithub.com\u002Faishwaryanr\u002Fawesome-generative-ai-guide#book-list-of-free-genai-courses))\n- Generative AI resources and roadmaps\n  - [3-day RAG roadmap](https:\u002F\u002Fgithub.com\u002Faishwaryanr\u002Fawesome-generative-ai-guide\u002Fblob\u002Fmain\u002Fresources\u002FRAG_roadmap.md)\n  - [5-day LLM foundations roadmap](https:\u002F\u002Fgithub.com\u002Faishwaryanr\u002Fawesome-generative-ai-guide\u002Fblob\u002Fmain\u002Fresources\u002Fgenai_roadmap.md)\n  - [5-day LLM agents roadmap](https:\u002F\u002Fgithub.com\u002Faishwaryanr\u002Fawesome-generative-ai-guide\u002Fblob\u002Fmain\u002Fresources\u002Fagents_roadmap.md)\n  - [Agents 101 guide](https:\u002F\u002Fgithub.com\u002Faishwaryanr\u002Fawesome-generative-ai-guide\u002Fblob\u002Fmain\u002Fresources\u002Fagents_101_guide.md)\n  - [Introduction to MM LLMs](https:\u002F\u002Fgithub.com\u002Faishwaryanr\u002Fawesome-generative-ai-guide\u002Fblob\u002Fmain\u002Fresources\u002Fmm_llms_guide.md)\n  - [LLM Lingo Series: Commonly used LLM terms and their easy-to-understand definitions](https:\u002F\u002Fgithub.com\u002Faishwaryanr\u002Fawesome-generative-ai-guide\u002Fblob\u002Fmain\u002Fresources\u002Fllm_lingo)\n\n---\n\n\n## :mortar_board: Courses\n\n#### [Ongoing] Applied LLMs Mastery 2024\n\nJoin 1000+ students on this 10-week adventure as we delve into the application of LLMs across a variety of use cases\n\n#### [Link](https:\u002F\u002Fareganti.notion.site\u002FApplied-LLMs-Mastery-2024-562ddaa27791463e9a1286199325045c) to the course website\n\n##### [Feb 2024] Registrations are still open [click here](https:\u002F\u002Fforms.gle\u002F353sQMRvS951jDYu7) to register\n\n🗓️\\*Week 1 [Jan 15 2024]**\\*: [Practical Introduction to LLMs](https:\u002F\u002Fgithub.com\u002Faishwaryanr\u002Fawesome-generative-ai-guide\u002Fblob\u002Fmain\u002Ffree_courses\u002FApplied_LLMs_Mastery_2024\u002Fweek1_part1_foundations.md)**\n\n- Applied LLM Foundations\n- Real World LLM Use Cases\n- Domain and Task Adaptation Methods\n\n🗓️\\*Week 2 [Jan 22 2024]**\\*: [Prompting and Prompt\nEngineering](https:\u002F\u002Fgithub.com\u002Faishwaryanr\u002Fawesome-generative-ai-guide\u002Fblob\u002Fmain\u002Ffree_courses\u002FApplied_LLMs_Mastery_2024\u002Fweek2_prompting.md)**\n\n- Basic Prompting Principles\n- Types of Prompting\n- Applications, Risks and Advanced Prompting\n\n🗓️\\*Week 3 [Jan 29 2024]**\\*: [LLM Fine-tuning](https:\u002F\u002Fgithub.com\u002Faishwaryanr\u002Fawesome-generative-ai-guide\u002Fblob\u002Fmain\u002Ffree_courses\u002FApplied_LLMs_Mastery_2024\u002Fweek3_finetuning_llms.md)**\n\n- Basics of Fine-Tuning\n- Types of Fine-Tuning\n- Fine-Tuning Challenges\n\n🗓️\\*Week 4 [Feb 5 2024]**\\*: [RAG (Retrieval-Augmented Generation)](https:\u002F\u002Fgithub.com\u002Faishwaryanr\u002Fawesome-generative-ai-guide\u002Fblob\u002Fmain\u002Ffree_courses\u002FApplied_LLMs_Mastery_2024\u002Fweek4_RAG.md)**\n\n- Understanding the concept of RAG in LLMs\n- Key components of RAG\n- Advanced RAG Methods\n\n🗓️\\*Week 5 [ Feb 12 2024]**\\*: [Tools for building LLM Apps](https:\u002F\u002Fgithub.com\u002Faishwaryanr\u002Fawesome-generative-ai-guide\u002Fblob\u002Fmain\u002Ffree_courses\u002FApplied_LLMs_Mastery_2024\u002Fweek5_tools_for_LLM_apps.md)**\n\n- Fine-tuning Tools\n- RAG Tools\n- Tools for observability, prompting, serving, vector search etc.\n\n🗓️\\*Week 6 [Feb 19 2024]**\\*: [Evaluation Techniques](https:\u002F\u002Fgithub.com\u002Faishwaryanr\u002Fawesome-generative-ai-guide\u002Fblob\u002Fmain\u002Ffree_courses\u002FApplied_LLMs_Mastery_2024\u002Fweek6_llm_evaluation.md)**\n\n- Types of Evaluation\n- Common Evaluation Benchmarks\n- Common Metrics\n\n🗓️\\*Week 7 [Feb 26 2024]**\\*: [Building Your Own LLM Application](https:\u002F\u002Fgithub.com\u002Faishwaryanr\u002Fawesome-generative-ai-guide\u002Fblob\u002Fmain\u002Ffree_courses\u002FApplied_LLMs_Mastery_2024\u002Fweek7_build_llm_app.md)**\n\n- Components of LLM application\n- Build your own LLM App end to end\n\n🗓️\\*Week 8 [March 4 2024]**\\*: [Advanced Features and Deployment](https:\u002F\u002Fgithub.com\u002Faishwaryanr\u002Fawesome-generative-ai-guide\u002Fblob\u002Fmain\u002Ffree_courses\u002FApplied_LLMs_Mastery_2024\u002Fweek8_advanced_features.md)**\n\n- LLM lifecycle and LLMOps\n- LLM Monitoring and Observability\n- Deployment strategies\n\n🗓️\\*Week 9 [March 11 2024]**\\*: [Challenges with LLMs](https:\u002F\u002Fgithub.com\u002Faishwaryanr\u002Fawesome-generative-ai-guide\u002Fblob\u002Fmain\u002Ffree_courses\u002FApplied_LLMs_Mastery_2024\u002Fweek9_challenges_with_llms.md)**\n\n- Scaling Challenges\n- Behavioral Challenges\n- Future directions\n\n🗓️\\*Week 10 [March 18 2024]**\\*: [Emerging Research Trends](https:\u002F\u002Fgithub.com\u002Faishwaryanr\u002Fawesome-generative-ai-guide\u002Fblob\u002Fmain\u002Ffree_courses\u002FApplied_LLMs_Mastery_2024\u002Fweek10_research_trends.md)**\n\n- Smaller and more performant models\n- Multimodal models\n- LLM Alignment\n\n🗓️*Week 11 *Bonus\\* [March 25 2024]**\\*: [Foundations](https:\u002F\u002Fgithub.com\u002Faishwaryanr\u002Fawesome-generative-ai-guide\u002Fblob\u002Fmain\u002Ffree_courses\u002FApplied_LLMs_Mastery_2024\u002Fweek11_foundations.md)**\n\n- Generative Models Foundations\n- Self-Attention and Transformers\n- Neural Networks for Language\n\n---\n\n#### :book: List of Free GenAI Courses\n\n##### LLM Basics and Foundations\n\n1. [Large Language Models](https:\u002F\u002Frycolab.io\u002Fclasses\u002Fllm-s23\u002F) by ETH Zurich\n\n2. [Understanding Large Language Models](https:\u002F\u002Fwww.cs.princeton.edu\u002Fcourses\u002Farchive\u002Ffall22\u002Fcos597G\u002F) by Princeton\n\n3. [Transformers course](https:\u002F\u002Fhuggingface.co\u002Flearn\u002Fnlp-course\u002Fchapter1\u002F1) by Huggingface\n\n4. [NLP course](https:\u002F\u002Fhuggingface.co\u002Flearn\u002Fnlp-course\u002Fchapter1\u002F1) by Huggingface\n\n5. [CS324 - Large Language Models](https:\u002F\u002Fstanford-cs324.github.io\u002Fwinter2022\u002F) by Stanford\n\n6. [Generative AI with Large Language Models](https:\u002F\u002Fwww.coursera.org\u002Flearn\u002Fgenerative-ai-with-llms) by Coursera\n\n7. [Introduction to Generative AI](https:\u002F\u002Fwww.coursera.org\u002Flearn\u002Fintroduction-to-generative-ai) by Coursera\n\n8. [Generative AI Fundamentals](https:\u002F\u002Fwww.cloudskillsboost.google\u002Fpaths\u002F118\u002Fcourse_templates\u002F556) by Google Cloud\n9. [5-Day Gen AI Intensive Course](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=kpRyiJUUFxY&list=PLqFaTIg4myu-b1PlxitQdY0UYIbys-2es) by Google & Kaggle\n\n10. [Introduction to Large Language Models](https:\u002F\u002Fwww.cloudskillsboost.google\u002Fpaths\u002F118\u002Fcourse_templates\u002F539) by Google Cloud\n11. [Introduction to Generative AI](https:\u002F\u002Fwww.cloudskillsboost.google\u002Fpaths\u002F118\u002Fcourse_templates\u002F536) by Google Cloud\n12. [Generative AI Concepts](https:\u002F\u002Fwww.datacamp.com\u002Fcourses\u002Fgenerative-ai-concepts) by DataCamp (Daniel Tedesco Data Lead @ Google)\n13. [1 Hour Introduction to LLM (Large Language Models)](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=xu5_kka-suc) by WeCloudData\n14. [LLM Foundation Models from the Ground Up | Primer](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=W0c7jQezTDw&list=PLTPXxbhUt-YWjMCDahwdVye8HW69p5NYS) by Databricks\n15. [Generative AI Explained](https:\u002F\u002Fcourses.nvidia.com\u002Fcourses\u002Fcourse-v1:DLI+S-FX-07+V1\u002F) by Nvidia\n16. [Transformer Models and BERT Model](https:\u002F\u002Fwww.cloudskillsboost.google\u002Fcourse_templates\u002F538) by Google Cloud\n17. [Generative AI Learning Plan for Decision Makers](https:\u002F\u002Fexplore.skillbuilder.aws\u002Flearn\u002Fpublic\u002Flearning_plan\u002Fview\u002F1909\u002Fgenerative-ai-learning-plan-for-decision-makers) by AWS\n18. [Introduction to Responsible AI](https:\u002F\u002Fwww.cloudskillsboost.google\u002Fcourse_templates\u002F554) by Google Cloud\n19. [Fundamentals of Generative AI](https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Ftraining\u002Fmodules\u002Ffundamentals-generative-ai\u002F) by Microsoft Azure\n20. [Generative AI for Beginners](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fgenerative-ai-for-beginners?WT.mc_id=academic-122979-leestott) by Microsoft\n21. [ChatGPT for Beginners: The Ultimate Use Cases for Everyone](https:\u002F\u002Fwww.udemy.com\u002Fcourse\u002Fchatgpt-for-beginners-the-ultimate-use-cases-for-everyone\u002F) by Udemy\n22. [[1hr Talk] Intro to Large Language Models](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=zjkBMFhNj_g) by Andrej Karpathy\n23. [ChatGPT for Everyone](https:\u002F\u002Flearnprompting.org\u002Fcourses\u002Fchatgpt-for-everyone) by Learn Prompting\n24. [Large Language Models (LLMs) (In English)](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLxlkzujLkmQ9vMaqfvqyfvZV_o8EqjAk7) by Kshitiz Verma (JK Lakshmipat University, Jaipur, India)\n25. [Generative AI for Beginners](https:\u002F\u002Fcodekidz.ai\u002Flesson-intro\u002Fgenerative-a-362093) By CodeKidz, based on Microsoft's open sourced course.\n\n##### Building LLM Applications\n\n1. [LLMOps: Building Real-World Applications With Large Language Models](https:\u002F\u002Fwww.udacity.com\u002Fcourse\u002Fbuilding-real-world-applications-with-large-language-models--cd13455) by Udacity\n\n2. [Full Stack LLM Bootcamp](https:\u002F\u002Ffullstackdeeplearning.com\u002Fllm-bootcamp\u002F) by FSDL\n\n3. [Generative AI for beginners](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fgenerative-ai-for-beginners\u002Ftree\u002Fmain) by Microsoft\n\n4. [Large Language Models: Application through Production](https:\u002F\u002Fwww.edx.org\u002Flearn\u002Fcomputer-science\u002Fdatabricks-large-language-models-application-through-production) by Databricks\n\n5. [Generative AI Foundations](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=oYm66fHqHUM&list=PLhr1KZpdzukf-xb0lmiU3G89GJXaDbAIF) by AWS\n\n6. [Introduction to Generative AI Community Course](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=ajWheP8ZD70&list=PLmQAMKHKeLZ-iTT-E2kK9uePrJ1Xua9VL) by ineuron\n\n7. [LLM University](https:\u002F\u002Fdocs.cohere.com\u002Fdocs\u002Fllmu) by Cohere\n8. [LLM Learning Lab](https:\u002F\u002Flightning.ai\u002Fpages\u002Fllm-learning-lab\u002F) by Lightning AI\n9. [LangChain for LLM Application Development](https:\u002F\u002Flearn.deeplearning.ai\u002Flogin?redirect_course=langchain&callbackUrl=https%3A%2F%2Flearn.deeplearning.ai%2Fcourses%2Flangchain) by Deeplearning.AI\n10. [LLMOps](https:\u002F\u002Flearn.deeplearning.ai\u002Fllmops) by DeepLearning.AI\n11. [Automated Testing for LLMOps](https:\u002F\u002Flearn.deeplearning.ai\u002Fautomated-testing-llmops) by DeepLearning.AI\n12. [Building Generative AI Applications Using Amazon Bedrock](https:\u002F\u002Fexplore.skillbuilder.aws\u002Flearn\u002Fcourse\u002Fexternal\u002Fview\u002Felearning\u002F17904\u002Fbuilding-generative-ai-applications-using-amazon-bedrock-aws-digital-training) by AWS\n13. [Efficiently Serving LLMs](https:\u002F\u002Flearn.deeplearning.ai\u002Fcourses\u002Fefficiently-serving-llms\u002Flesson\u002F1\u002Fintroduction) by DeepLearning.AI\n14. [Building Systems with the ChatGPT API](https:\u002F\u002Fwww.deeplearning.ai\u002Fshort-courses\u002Fbuilding-systems-with-chatgpt\u002F) by DeepLearning.AI\n15. [Serverless LLM apps with Amazon Bedrock](https:\u002F\u002Fwww.deeplearning.ai\u002Fshort-courses\u002Fserverless-llm-apps-amazon-bedrock\u002F) by DeepLearning.AI\n16. [Building Applications with Vector Databases](https:\u002F\u002Fwww.deeplearning.ai\u002Fshort-courses\u002Fbuilding-applications-vector-databases\u002F) by DeepLearning.AI\n17. [Automated Testing for LLMOps](https:\u002F\u002Fwww.deeplearning.ai\u002Fshort-courses\u002Fautomated-testing-llmops\u002F) by DeepLearning.AI\n18. [Build LLM Apps with LangChain.js](https:\u002F\u002Fwww.deeplearning.ai\u002Fshort-courses\u002Fbuild-llm-apps-with-langchain-js\u002F) by DeepLearning.AI\n19. [Advanced Retrieval for AI with Chroma](https:\u002F\u002Fwww.deeplearning.ai\u002Fshort-courses\u002Fadvanced-retrieval-for-ai\u002F) by DeepLearning.AI\n20. [Operationalizing LLMs on Azure](https:\u002F\u002Fwww.coursera.org\u002Flearn\u002Fllmops-azure) by Coursera\n21. [Generative AI Full Course – Gemini Pro, OpenAI, Llama, Langchain, Pinecone, Vector Databases & More](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=mEsleV16qdo) by freeCodeCamp.org\n22. [Training & Fine-Tuning LLMs for Production](https:\u002F\u002Flearn.activeloop.ai\u002Fcourses\u002Fllms) by Activeloop\n    \n\n##### Prompt Engineering, RAG and Fine-Tuning\n\n1. [LangChain & Vector Databases in Production](https:\u002F\u002Fwww.youtube.com\u002Fredirect?event=video_description&redir_token=QUFFLUhqbVhnQW8xNDdhSU9IUDVLXzFhV2N0UkNRMkZrQXxBQ3Jtc0traUxHMzZJcGJQYjlyckYxaGxYVWlsOFNGUFlFVEdhNzdjTWpPUlQ2TF9XczRqNkxMVGpJTnd5YmYzV0prQ0IwZURNcHhIZ3h1Z051VTl5MXBBLUN0dkM0NHRkQTFua1Jpc0VCRFJUb0ZQZG95b0JqMA&q=https%3A%2F%2Flearn.activeloop.ai%2Fcourses%2Flangchain&v=gKUTDC13jys) by Activeloop\n\n2. [Reinforcement Learning from Human Feedback](https:\u002F\u002Flearn.deeplearning.ai\u002Freinforcement-learning-from-human-feedback) by DeepLearning.AI\n\n3. [Building Applications with Vector Databases](https:\u002F\u002Flearn.deeplearning.ai\u002Fbuilding-applications-vector-databases) by DeepLearning.AI\n\n4. [Finetuning Large Language Models](https:\u002F\u002Flearn.deeplearning.ai\u002Ffinetuning-large-language-models) by Deeplearning.AI\n5. [LangChain: Chat with Your Data](http:\u002F\u002Flearn.deeplearning.ai\u002Flangchain-chat-with-your-data\u002F) by Deeplearning.AI\n\n6. [Building Systems with the ChatGPT API](https:\u002F\u002Flearn.deeplearning.ai\u002Fchatgpt-building-system) by Deeplearning.AI\n7. [Prompt Engineering with Llama 2](https:\u002F\u002Fwww.deeplearning.ai\u002Fshort-courses\u002Fprompt-engineering-with-llama-2\u002F) by Deeplearning.AI\n8. [Building Applications with Vector Databases](https:\u002F\u002Flearn.deeplearning.ai\u002Fbuilding-applications-vector-databases) by Deeplearning.AI\n9. [ChatGPT Prompt Engineering for Developers](https:\u002F\u002Flearn.deeplearning.ai\u002Fchatgpt-prompt-eng\u002Flesson\u002F1\u002Fintroduction) by Deeplearning.AI\n10. [Advanced RAG Orchestration series](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=CeDS1yvw9E4) by LlamaIndex\n11. [Prompt Engineering Specialization](https:\u002F\u002Fwww.coursera.org\u002Fspecializations\u002Fprompt-engineering) by Coursera\n12. [Augment your LLM Using Retrieval Augmented Generation](https:\u002F\u002Fcourses.nvidia.com\u002Fcourses\u002Fcourse-v1:NVIDIA+S-FX-16+v1\u002F) by Nvidia\n13. [Knowledge Graphs for RAG](https:\u002F\u002Fwww.deeplearning.ai\u002Fshort-courses\u002Fknowledge-graphs-rag\u002F) by Deeplearning.AI\n14. [Open Source Models with Hugging Face](https:\u002F\u002Fwww.deeplearning.ai\u002Fshort-courses\u002Fopen-source-models-hugging-face\u002F) by Deeplearning.AI\n15. [Vector Databases: from Embeddings to Applications](https:\u002F\u002Fwww.deeplearning.ai\u002Fshort-courses\u002Fvector-databases-embeddings-applications\u002F) by Deeplearning.AI\n16. [Understanding and Applying Text Embeddings](https:\u002F\u002Fwww.deeplearning.ai\u002Fshort-courses\u002Fgoogle-cloud-vertex-ai\u002F) by Deeplearning.AI\n17. [JavaScript RAG Web Apps with LlamaIndex](https:\u002F\u002Fwww.deeplearning.ai\u002Fshort-courses\u002Fjavascript-rag-web-apps-with-llamaindex\u002F) by Deeplearning.AI\n18. [Quantization Fundamentals with Hugging Face](https:\u002F\u002Fwww.deeplearning.ai\u002Fshort-courses\u002Fquantization-fundamentals-with-hugging-face\u002F) by Deeplearning.AI\n19. [Preprocessing Unstructured Data for LLM Applications](https:\u002F\u002Fwww.deeplearning.ai\u002Fshort-courses\u002Fpreprocessing-unstructured-data-for-llm-applications\u002F) by Deeplearning.AI\n20. [Retrieval Augmented Generation for Production with LangChain & LlamaIndex](https:\u002F\u002Flearn.activeloop.ai\u002Fcourses\u002Frag) by Activeloop\n21. [Quantization in Depth](https:\u002F\u002Fwww.deeplearning.ai\u002Fshort-courses\u002Fquantization-in-depth\u002F) by Deeplearning.AI\n\n##### Evaluation\n\n1. [Building and Evaluating Advanced RAG Applications](https:\u002F\u002Flearn.deeplearning.ai\u002Fbuilding-evaluating-advanced-rag) by DeepLearning.AI\n2. [Evaluating and Debugging Generative AI Models Using Weights and Biases](https:\u002F\u002Flearn.deeplearning.ai\u002Fevaluating-debugging-generative-ai) by Deeplearning.AI\n3. [Quality and Safety for LLM Applications](https:\u002F\u002Fwww.deeplearning.ai\u002Fshort-courses\u002Fquality-safety-llm-applications\u002F) by Deeplearning.AI\n4. [Red Teaming LLM Applications](https:\u002F\u002Fwww.deeplearning.ai\u002Fshort-courses\u002Fred-teaming-llm-applications\u002F?utm_campaign=giskard-launch&utm_medium=headband&utm_source=dlai-homepage) by Deeplearning.AI\n\n##### Multimodal\n\n1. [How Diffusion Models Work](https:\u002F\u002Fwww.deeplearning.ai\u002Fshort-courses\u002Fhow-diffusion-models-work\u002F) by DeepLearning.AI\n2. [How to Use Midjourney, AI Art and ChatGPT to Create an Amazing Website](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=5wdCev86RYE) by Brad Hussey\n3. [Build AI Apps with ChatGPT, DALL-E and GPT-4](https:\u002F\u002Fscrimba.com\u002Flearn\u002Fbuildaiapps) by Scrimba\n4. [11-777: Multimodal Machine Learning](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL-Fhd_vrvisNM7pbbevXKAbT_Xmub37fA) by Carnegie Mellon University\n5. [Prompt Engineering for Vision Models](https:\u002F\u002Fwww.deeplearning.ai\u002Fshort-courses\u002Fprompt-engineering-for-vision-models\u002F) by Deeplearning.AI\n\n##### Agents\n1. [Building RAG Agents with LLMs](https:\u002F\u002Fcourses.nvidia.com\u002Fcourses\u002Fcourse-v1:DLI+S-FX-15+V1\u002F) by Nvidia\n2. [Functions, Tools and Agents with LangChain](https:\u002F\u002Flearn.deeplearning.ai\u002Ffunctions-tools-agents-langchain) by Deeplearning.AI\n3. [AI Agents in LangGraph](https:\u002F\u002Fwww.deeplearning.ai\u002Fshort-courses\u002Fai-agents-in-langgraph\u002F) by Deeplearning.AI\n4. [AI Agentic Design Patterns with AutoGen](https:\u002F\u002Fwww.deeplearning.ai\u002Fshort-courses\u002Fai-agentic-design-patterns-with-autogen\u002F) by Deeplearning.AI\n5. [Multi AI Agent Systems with crewAI](https:\u002F\u002Fwww.deeplearning.ai\u002Fshort-courses\u002Fmulti-ai-agent-systems-with-crewai\u002F) by Deeplearning.AI\n6. [Building Agentic RAG with LlamaIndex](https:\u002F\u002Fwww.deeplearning.ai\u002Fshort-courses\u002Fbuilding-agentic-rag-with-llamaindex\u002F) by Deeplearning.AI\n7. [LLM Observability: Agents, Tools, and Chains](https:\u002F\u002Fcourses.arize.com\u002Fp\u002Fagents-tools-and-chains) by Arize AI\n8. [Building Agentic RAG with LlamaIndex](https:\u002F\u002Fwww.deeplearning.ai\u002Fshort-courses\u002Fbuilding-agentic-rag-with-llamaindex\u002F) by Deeplearning.AI\n9. [Agents Tools & Function Calling with Amazon Bedrock (How-to)](https:\u002F\u002Fwww.youtube.com\u002Fwatch?app=desktop&v=2L_XE6g3atI) by AWS Developers\n10. [ChatGPT & Zapier: Agentic AI for Everyone](https:\u002F\u002Fwww.coursera.org\u002Flearn\u002Fagentic-ai-chatgpt-zapier) by Coursera\n11. [Multi-Agent Systems with AutoGen](https:\u002F\u002Fwww.manning.com\u002Fbooks\u002Fmulti-agent-systems-with-autogen-cx) by Victor Dibia [Book]\n12. [Large Language Model Agents MOOC, Fall 2024](https:\u002F\u002Fllmagents-learning.org\u002Ff24) by Dawn Song & Xinyun Chen – A comprehensive course covering foundational and advanced topics on LLM agents.\n13. [CS294\u002F194-196 Large Language Model Agents](https:\u002F\u002Frdi.berkeley.edu\u002Fllm-agents\u002Ff24) by UC Berkeley\n\n\n\n\n\n#### Miscellaneous\n\n1. [Avoiding AI Harm](https:\u002F\u002Fwww.coursera.org\u002Flearn\u002Favoiding-ai-harm) by Coursera\n2. [Developing AI Policy](https:\u002F\u002Fwww.coursera.org\u002Flearn\u002Fdeveloping-ai-policy) by Coursera\n\n---\n\n## :paperclip: Resources\n\n- [ICLR 2024 Paper Summaries](https:\u002F\u002Fareganti.notion.site\u002F06f0d4fe46a94d62bff2ae001cfec22c?v=d501ca62e4b745768385d698f173ae14)\n\n---\n\n## :computer: Interview Prep\n\n#### Topic wise Questions:\n\n1. [Common GenAI Interview Questions](https:\u002F\u002Fgithub.com\u002Faishwaryanr\u002Fawesome-generative-ai-guide\u002Fblob\u002Fmain\u002Finterview_prep\u002F60_gen_ai_questions.md)\n2. Prompting and Prompt Engineering\n3. Model Fine-Tuning\n4. Model Evaluation\n5. MLOps for GenAI\n6. Generative Models Foundations\n7. Latest Research Trends\n\n#### GenAI System Design (Coming Soon):\n\n1. Designing an LLM-Powered Search Engine\n2. Building a Customer Support Chatbot\n3. Building a system for natural language interaction with your data.\n4. Building an AI Co-pilot\n5. Designing a Custom Chatbot for Q\u002FA on Multimodal Data (Text, Images, Tables, CSV Files)\n6. Building an Automated Product Description and Image Generation System for E-commerce\n\n---\n\n## :notebook: Code Notebooks\n\n#### RAG Tutorials\n\n- [AWS Bedrock Workshop Tutorials](https:\u002F\u002Fgithub.com\u002Faws-samples\u002Famazon-bedrock-workshop) by Amazon Web Services\n- [Langchain Tutorials](https:\u002F\u002Fgithub.com\u002Fgkamradt\u002Flangchain-tutorials) by gkamradt\n- [LLM Applications for production](https:\u002F\u002Fgithub.com\u002Fray-project\u002Fllm-applications\u002Ftree\u002Fmain) by ray-project\n- [LLM tutorials](https:\u002F\u002Fgithub.com\u002Follama\u002Follama\u002Ftree\u002Fmain\u002Fexamples) by Ollama\n- [LLM Hub](https:\u002F\u002Fgithub.com\u002Fmallahyari\u002Fllm-hub) by mallahyari\n- [RAG cookbook](https:\u002F\u002Fdocs.camel-ai.org\u002Fcookbooks\u002Fagents_with_rag.html) by CAMEL-AI\n\n#### Fine-Tuning Tutorials\n\n- [LLM Fine-tuning tutorials](https:\u002F\u002Fgithub.com\u002Fashishpatel26\u002FLLM-Finetuning) by ashishpatel26\n- [PEFT](https:\u002F\u002Fgithub.com\u002Fhuggingface\u002Fpeft\u002Ftree\u002Fmain\u002Fexamples) example notebooks by Huggingface\n- [Free LLM Fine-Tuning Notebooks](https:\u002F\u002Flevelup.gitconnected.com\u002F14-free-large-language-models-fine-tuning-notebooks-532055717cb7) by Youssef Hosni\n\n\n#### Comprehensive LLM Code Repositories \n- [LLM-PlayLab](https:\u002F\u002Fgithub.com\u002FSakil786\u002FLLM-PlayLab) This playlab encompasses a multitude of projects crafted through the utilization of Transformer Models\n\n\n---\n\n## :black_nib: Contributing\n\nIf you want to add to the repository or find any issues, please feel free to raise a PR and ensure correct placement within the relevant section or category.\n\n---\n\n## :pushpin: Cite Us\n\nTo cite this guide, use the below format:\n\n```\n@article{areganti_generative_ai_guide,\nauthor = {Reganti, Aishwarya Naresh},\njournal = {https:\u002F\u002Fgithub.com\u002Faishwaryanr\u002Fawesome-generative-ai-resources},\nmonth = {01},\ntitle = {{Generative AI Guide}},\nyear = {2024}\n}\n```\n\n## License\n\n[MIT License]\n\n\n\n\u003Csup>**\u003C\u002Fsup> This section is sponsored. We do not endorse or guarantee the product\u002Fservice and are not responsible for any issues arising from its use. Please evaluate and use at your discretion.\n","awesome-generative-ai-guide是一个专注于生成式AI研究更新、面试资源、笔记本等内容的综合性资源库。该项目提供了每月最佳生成式AI论文列表、面试准备资料以及多个免费课程，包括应用大语言模型精通课程和生成式AI天才课程等，同时还有超过90个与生成式AI相关的免费课程列表及代码仓库\u002F笔记本链接供开发者参考学习。适用于对生成式AI感兴趣的研究人员、开发人员以及希望提升相关技能的学习者，在项目中可以找到从理论到实践所需的各种资源。",2,"2026-06-11 03:45:59","high_star"]