This course provides a complete introduction to Retrieval-Augmented Generation (RAG), one of the most important techniques for improving large language models by connecting them with external data sources. The series is designed to build your understanding from the ground up, starting with fundamental concepts and progressing toward advanced query and retrieval strategies.

You begin with a clear overview of what RAG is and why it is essential for modern AI systems. It explains how RAG improves LLM performance by allowing models to access up-to-date and domain-specific information instead of relying only on pre-trained knowledge.

The course then breaks down the full RAG pipeline into core components: indexing, retrieval, and generation. You will learn how data is stored, how relevant information is retrieved from external sources, and how it is used to generate accurate responses.

Advanced sections explore query transformation techniques such as multi-query, RAG fusion, decomposition, and step-back reasoning. These methods help improve search quality and answer accuracy by refining how user queries are processed.

By the end of this series, you will understand how to design and build complete RAG systems using LangChain, enabling you to create AI applications that are more intelligent, reliable, and context-aware.