Retrieval Augmented Generation (RAG)

Retrieval Augmented Generation (RAG) improves large language model (LLM) responses by retrieving relevant data from knowledge bases—often private, recent, or domain-specific—and using it to generate more accurate, grounded answers. In this course, you’ll learn how to build RAG systems that connect LLMs to external data sources. You’ll explore core components like retrievers, vector databases, and language models, and apply key techniques at both the component and system level. Through hands-on

• Design and build RAG systems tailored to real-world needs
• Assess tradeoffs between cost, speed, and quality for RAG components
• Develop frameworks to adapt RAG systems as tools and methods evolve

• Basic understanding of large language model techniques
• Familiarity with vector databases