This hands-on course teaches how to build real-world Retrieval-Augmented Generation (RAG) systems and deploy production-grade AI applications using modern LLM frameworks. It is designed for developers and engineers who want to move beyond theory and build scalable AI systems.
The course starts with an introduction to building your first RAG application using tools like LangChain, Chainlit, and Hugging Face. You will learn how retrieval systems work and how they improve the accuracy and reliability of Large Language Models.
Next, the course explores advanced RAG architectures using LangChain agents and LlamaIndex data agents, showing how to enhance reasoning and improve information retrieval across complex datasets.
You will also learn how to design production-level systems using Llama 2, FastAPI, and scalable backend architectures. This includes building APIs for LLM applications and deploying them efficiently.
The course covers evaluation and monitoring techniques using tools like RAGAS, LangSmith, Weights & Biases, and EleutherAI evaluation frameworks. These tools help measure performance and ensure system reliability.
Finally, learners will explore LLMOps concepts such as observability, scalability, and fine-tuning RAG pipelines for real-world use cases.
By the end of this course, you will be able to design, build, evaluate, and deploy full RAG-based AI systems ready for production environments.
