Building RAG Systems with Vector Databases

Want to build intelligent search and chatbot systems that understand context? Vector databases are the essential technology powering modern large language model applications. This course provides a practical, foundational understanding of vector databases and how to use them with Retrieval Augmented Generation (RAG). You will learn the core concepts behind embeddings, similarity search, and deploying a functional knowledge-based chatbot and search API. What you'll learn: Understand the principles of vector embeddings, semantic similarity, and efficient indexing methods. Configure and deploy core vector database platforms, including Qdrant, Weaviate, and the FAISS library. Master the workflow for Retrieval Augmented Generation (RAG) to connect LLMs to your private data sources. Practice creating, indexing, and querying documents within various vector stores. Build a basic semantic search API and a functional RAG-based chatbot prototype. Apply fundamental concepts for monitoring and evaluating the performance of search and retrieval systems. The course begins with foundational definitions and concepts, progressing through practical setup and hands-on implementation exercises. We focus purely on the written explanations and code necessary to achieve practical results. This course is designed for beginners in AI application development who want to understand the modern architecture necessary to build LLM-powered applications. No prior knowledge of vector databases is required. Start building the next generation of intelligent applications today.