Complete Generative AI Course: RAG, AI Agents & Deployment (Udemy.com)
Learn Generative AI from scratch – Build RAG, AI Agents & Chatbots, master MCP, and deploy real-world projects
Created by: Siddhardhan S
Last updated May 2026
What you will learn
- Master the foundations of Generative AI, Large Language Models, and Transformer architecture.
- Build real-world AI applications including chatbots, RAG systems, MCP servers, and multi-agent systems.
- Deploy LLM-powered solutions on the cloud using Docker, Streamlit, Ollama, vLLM, and AWS EC2.
- Gain the knowledge and hands-on skills required to step into a Generative AI Engineer role.
How it compares
Against the other 461 rated Udemy courses in Data Science on CourseDuck:
- Rating4.4 out of 5higher than 28% of them
- Reviews2,363#83 of 462 by number of reviews
- Length23.2 hourslonger than 85% of them
- Last updatedMay 2026more recently than 75% of them
Course Description
This complete Generative AI course takes you from beginner to advanced with hands-on projects, real-world applications, and career-ready skills. You’ll learn the foundations of Generative AI, explore Large Language Models (LLMs), master frameworks like LangChain, LlamaIndex, CrewAI, and PydanticAI, and deploy your own AI solutions on the cloud. The course is tailored to equip you with both the knowledge and practical experience required to step into a Generative AI Engineer role.
Each section includes quizzes & coding exercises to help you test your knowledge and reinforce your skills.
What you’ll learn in each section
1. Introduction – Get started with the course, understand what you will learn & set up Python environments (Colab, Jupyter, PyCharm).
2. Generative AI – Foundation – Understand AI vs ML vs DL vs GenAI, dive into Large Language Models, and learn the Transformer architecture.
3. Accessing LLMs in Python – Use OpenAI, Gemini, Groq, and Ollama LLMs, and connect them through LangChain and LlamaIndex.
4. Prompt Engineering – Explore prompt templates, zero-shot, and few-shot prompting to effectively interact with LLMs.
5. Building GenAI Chatbots – Build and deploy chatbots step by step using LangChain, LlamaIndex, Streamlit UI, and Streamlit Cloud.
6. Retrieval-Augmented Generation (RAG) – Understand RAG, build RAG pipelines with LangChain and LlamaIndex, and create a PDF Q&A bot.
7. AI Agents – Learn what AI agents are and build agents with PydanticAI, AutoGen, and CrewAI for multi-agent workflows.
8. LLM Deployment – Deploy open-source LLMs with Ollama, Docker, and vLLM, and set them up on AWS EC2 for real-world usage.
9. Model Context Protocol (MCP) – Understand MCP, build an MCP server, and integrate MCP tools with PydanticAI and CrewAI agents.
10. Capstone Projects – Apply everything learned to build real-world AI projects: Enterprise Chatbots, RAG Assistants, and Intelligent AI Agents with Full Cloud Deployment.
Instructor Details
- 4.4 Rating
2,363 Reviews
Siddhardhan S
I am an experienced Generative AI Engineer & Instructor specializing in developing and deploying Machine Learning, Deep Learning, and Generative AI solutions across healthcare, finance, and enterprise applications. I have worked with multiple companies, delivering production-grade AI systems involving autonomous agents, Retrieval-Augmented Generation (RAG), large language models, and scalable cloud deployments on AWS.
Some of the impactful projects I have worked on include:
- AI-powered healthcare prediction models
- Supply chain optimization for global enterprises
- Large-scale LLM applications for code generation & document Q&A
- Graph-RAG based crime network analysis
- Voice-enabled healthcare assistants
- Financial Agentic AI chatbots for real-world use cases & more...
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Reviews
By Masoodh on 5/16/2026
Going moderate speed where all level members can understand easily. But excellent with more info giving for selecting which LLM model like an Architect.
By Nuria González Fernández on 4/8/2026
Good course overall if you are a begginer, because it covers the basic concepts that you need to know about the topic in a practical way, with hands-on projects you can work on. The course structure is also pretty intuitive and it gradually grows in complexity as you move through the sections. However, I missed more detailed explanations on the implementations and more complex projects that could raise my level of expertise to mid-level rather than junior.
By Sanat Dubey on 3/29/2026
It was really good, the learning was made very easy and each concept was taught in a nice and detailed way.
By Keshav Subramaniam on 3/24/2026
Exceptionally good course on Agentic AI - real world examples from beginner to experienced everyone can take the course. All concepts clearly explained indepth like RAG, MCP and Deployment on EC2.
By Valentina Iovino on 2/6/2026
I like it so far, but one thing to improve, is the speasks at light's speed, even if we can set subtitles , speaking this fast can affect the over-all understanding
By Metikal on 2/3/2026
level of explanation hits the roof and clinically clears each step. It would be more great if you have few flow diagrams just on the side so that intermediate people will able to catch the routing and relation between components involved.
By John Carpenter on 1/14/2026
I have a software dev background, trying to learn how to take advantage of current AI technology. I am getting a good overview at this point of how to access various models. Looking forward to learning more from this instructor. It would be nice to have an introductory tutorial on using jupyter notebooks.
By Kamil Meral on 12/30/2025
This guy is really explaining topics in a smooth way. Thank you brother! You should keep going on!! Please add new courses at ai agents too much. n8n etc..
By Damien Briggs on 12/14/2025
This is a very good, well structured course by someone who really knows a lot about the technology they are explaining. The explanations are clear and well presented.
By Ayush Dubey on 11/17/2025
Just completed section5, kind of disappointed. So far till now, i wish instead of just showing what to code you could focus MORE on the WHY part, which would help in building the intuition behind these things. why this certain function was used here, when do we use it, what does it do. Your focus is more on the what part, however, people that are beginners and wanna learn how to build, for them when and why is more crucial. Your ML course is such a gem. It was long, but still, by far the best ML course I've ever seen. There you took your time and explained each line and the WHY and WHEN behind it. You missed that kind of here. I will agree that now i know a few stuffs about GenAI, but i still will have to refer other resources to understand it in-depth. Kind of disappointed from this course so far, cant say for the remaining part. ill still complete it by next week and will let you know my overall feedback. I hope your upcoming DL course wouldn't be like this, i really wish you make it as lengthy as needed, core of the concept is much more important than just knowing how code lines work. It's the core concept which builds intuition and it takes time.
Quality Score
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Overall Score : 88 / 100







