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GenAI Roadmp

Updated: Feb 19, 2025

Level 1: Gen AI and RAG Foundations

1. Introduction to Generative AI
   • Generative AI for Everyone by Andrew Ng by DeepLearning.AI
   • https://www.deeplearning.ai/courses/generative-ai-for-everyone/
2. Basics of Large Language Models (LLMs)
   • H2O ai Large Language Models (LLMs) - Level 1 by Coursera and H2O.ai
   • https://www.coursera.org/learn/h2o-ai-large-language-models-llms-level-1
3. Fundamentals of Prompt Engineering
   • ChatGPT Prompt Engineering for Developers by DeepLearning.AI & OpenAI
   • https://www.deeplearning.ai/short-courses/chatgpt-prompt-engineering-for-developers/
4. Data Handling and Processing
   • Preprocessing Unstructured Data for LLM Apps by DeepLearning.AI & unstructured.io
   • https://www.deeplearning.ai/short-courses/preprocessing-unstructured-data-for-llm-applications/
5. Introduction to API Wrappers
   • Getting Started with Generative AI API Specialization by Coursera & Codio
   • https://www.coursera.org/specializations/codio-generative-ai
6. Essentials of RAG
   • Introduction to Retrieval Augmented Generation (RAG) by Coursera
   • https://www.coursera.org/learn/introduction-to-retrieval-augmented-generation-rag

Level 2: AI Agents Specialization

1. Introduction to AI Agents
   • Fundamentals of AI Agents Using RAG and LangChain by Coursera & IBM
   • https://www.coursera.org/learn/fundamentals-of-ai-agents-using-rag-and-langchain
2. Exploring Agent Frameworks
   • LangChain for LLM Application Development by DeepLearning.AI & LangChain
   • https://www.deeplearning.ai/short-courses/langchain-for-llm-application-development/
3. Building a Simple AI Agent
   • Build Autonomous AI Agents From Scratch With Python by Udemy
   • https://www.udemy.com/course/build-autonomous-ai-agents-from-scratch-with-python/
4. Understanding Agent Workflows
   • AI Agentic Design Patterns with AutoGen by Coursera & Microsoft
   • https://www.coursera.org/projects/ai-agentic-design-patterns-with-autogen
5. Learning About Agent Memory
   • LLMs as Operating Systems: Agent Memory by DeepLearning.AI & Letta
   • https://www.deeplearning.ai/short-courses/llms-as-operating-systems-agent-memory/
6. Evaluating Agent Performance
   • Building Intelligent Troubleshooting Agents by Coursera & Microsoft
   • https://www.coursera.org/learn/building-intelligent-troubleshooting-agents
7. Collaborating with Multiple Agents
   • Multi AI Agent Systems with crewAI by DeepLearning.AI & CrewAI
   • https://www.deeplearning.ai/short-courses/multi-ai-agent-systems-with-crewai/
8. Implementing RAG in Agents
   • Building & Evaluating Advanced RAG Apps by DeepLearning.AI, LlamaIndex & TruEra
   • https://www.deeplearning.ai/short-courses/building-evaluating-advanced-rag/

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Set 2:

⏩ 𝗣𝘆𝘁𝗵𝗼𝗻 𝘀𝗸𝗶𝗹𝗹𝘀 ✅ 𝗙𝗮𝘀𝘁𝗔𝗣𝗜 – Build lightweight, production-ready APIs in no time 🔗 Video1: https://www.youtube.com/watch?v=iWS9ogMPOI0 🔗 Video2: https://fastapi.tiangolo.com/tutorial/

✅ 𝗔𝘀𝘆𝗻𝗰 𝗣𝗿𝗼𝗴𝗿𝗮𝗺𝗺𝗶𝗻𝗴 – Speed up agents by handling multiple tasks asynchronously 🔗 Video: https://www.youtube.com/watch?v=Qb9s3UiMSTA

✅ 𝗣𝘆𝗱𝗮𝗻𝘁𝗶𝗰 – Data validation and settings management that keeps your agent robust and reliable 🔗 Video: https://www.youtube.com/watch?v=XIdQ6gO3Anc

✅ 𝗟𝗼𝗴𝗴𝗶𝗻𝗴 – Debugging agents without good logs? Nearly impossible. 🔗 Video: https://www.youtube.com/watch?v=9L77QExPmI0

✅ 𝗧𝗲𝘀𝘁𝗶𝗻𝗴 – Unit and integration tests will save you when agents become complex. 🔗 Unit test: https://www.youtube.com/watch?v=YbpKMIUjvK8 🔗 Integration test: https://www.youtube.com/watch?v=7dgQRVqF1N0

✅ 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 (SQLAlchemy + Alembic) – Essential for RAG and storing agent memories 🔗 Video: https://www.youtube.com/watch?v=i9RX03zFDHU

⏩ 𝗥𝗔𝗚 𝘀𝗸𝗶𝗹𝗹𝘀 ✅ 𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴 𝗥𝗔𝗚 – Learn what RAG is and why it matters 🔗 Video - https://www.youtube.com/watch?v=T-D1OfcDW1M

✅ 𝗧𝗲𝘅𝘁 𝗘𝗺𝗯𝗲𝗱𝗱𝗶𝗻𝗴𝘀 – Foundation of search and retrieval in RAG systems 🔗 Video - https://www.youtube.com/watch?v=vlcQV4j2kTo

✅ 𝗩𝗲𝗰𝘁𝗼𝗿 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲 – Store and retrieve embeddings efficiently 🔗 Video - https://www.youtube.com/watch?v=gl1r1XV0SLw

✅ 𝗖𝗵𝘂𝗻𝗸𝗶𝗻𝗴 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗲𝘀 – Split data smartly for better retrieval quality 🔗 Video - https://www.youtube.com/watch?v=8OJC21T2SL4

✅ 𝗥𝗔𝗚 𝘄𝗶𝘁𝗵 𝗣𝗼𝘀𝘁𝗴𝗿𝗲𝗦𝗤𝗟 – RAG without expensive vector DBs? Yes, possible. 🔗 Video - https://www.youtube.com/watch?v=hAdEuDBN57g

✅ 𝗥𝗔𝗚 𝘄𝗶𝘁𝗵 𝗟𝗮𝗻𝗴𝗖𝗵𝗮𝗶𝗻 – Chain together retrieval, LLMs, and prompts easily 🔗 Video - https://www.youtube.com/watch?v=sVcwVQRHIc8

✅ 𝗥𝗔𝗚 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻𝘀 – Know if your retrieval and answers are actually good 🔗 Video - https://www.youtube.com/watch?v=mEv-2Xnb_Wk

✅ 𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝗥𝗔𝗚 𝗧𝗲𝗰𝗵𝗻𝗶𝗾𝘂𝗲𝘀 – Fine-tune your RAG system for real-world performance 🔗 Video - https://www.youtube.com/watch?v=sGvXO7CVwc0

⏩ 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 ✅ 𝗟𝗮𝗻𝗴𝗚𝗿𝗮𝗽𝗵 – This one is mainly used in organisations to build production ready AI Agents. 🔗 Video - https://www.youtube.com/watch?v=jGg_1h0qzaM

✅ 𝗣𝗿𝗼𝗺𝗽𝘁 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗴𝘂𝗶𝗱𝗲 – It’s also good to follow specific guides from the LLM providers. 🔗 Link - https://github.com/dair-ai/Prompt-Engineering-Guide