Artificial Intelligence applications are moving beyond simple chatbots. Modern AI systems need access to private knowledge, company documents, and specialized information. Retrieval-Augmented Generation (RAG) is the technology that enables Large Language Models (LLMs) to answer questions using your own data.
In this comprehensive course, you will learn how to design, build, evaluate, and improve real-world RAG applications from the ground up. We start by understanding the foundations of Large Language Models, AI applications, embeddings, vector databases, and semantic search. You will learn how RAG works internally and why it has become one of the most important architectures for building reliable AI systems.
Throughout the course, you will explore the complete RAG pipeline:
- Preparing and processing documents
- Creating embeddings and understanding vector representations
- Building efficient retrieval systems
- Working with vector databases
- Designing RAG architectures
- Improving retrieval quality with advanced techniques
- Evaluating RAG performance
- Reducing hallucinations
- Optimizing accuracy, speed, and cost
- Designing production-ready AI applications
