RAG Systems and Vector Database Fundamentals

As AI applications move beyond simple chat interfaces, the ability to ground models in specific, private data is becoming an essential skill. Retrieval-Augmented Generation (RAG) allows you to bridge the gap between static models and dynamic information. This text-based course provides a structured path to understanding how RAG systems work and how to implement them. You will learn the mechanics of transforming text into mathematical vectors, storing them in specialized databases, and retrieving the most relevant information to generate precise AI responses. Through detailed written explanations and code examples, you will gain the knowledge needed to build reliable search and summarization tools. What you'll learn: - Understand the foundational concepts of vector embeddings and semantic search - Configure and use vector databases such as FAISS and Chroma DB for data management - Implement retrieval patterns to find the most relevant context for any query - Practice chunking strategies to prepare large datasets for efficient indexing - Apply prompt engineering basics to ground AI outputs in retrieved data - Explore modern RAG patterns including re-ranking and hybrid search The curriculum starts with essential terminology and the theory of vector space before progressing into the logic of building and optimizing retrieval pipelines. This course is designed for beginners looking to enter the field of AI engineering, with no previous experience in vector search required. Begin your journey into building intelligent, data-driven AI systems.