Building Agentic AI Systems with Vector Databases and RAG

Traditional AI models are limited by static training data, but modern intelligent systems must adapt and retrieve information dynamically. This text-based course guides you through the foundational concepts of Agentic AI, where autonomous agents make decisions and fetch real-time knowledge. By reading through our structured explanations and analyzing practical code snippets, you will understand how to connect Large Language Models to vector databases using Retrieval-Augmented Generation (RAG). You will transition from understanding simple prompts to architecting systems that can search, reason, and act. What you'll learn: - Understand the core architecture of autonomous Agentic AI and how it differs from standard LLM setups - Configure vector databases to store, index, and query high-dimensional document embeddings - Implement Retrieval-Augmented Generation patterns to ground AI responses in real-time data - Design agentic workflows using tool-use, planning, and self-reflection loops - Apply metadata filtering and semantic search techniques to improve retrieval accuracy - Practice writing clean orchestration code through written step-by-step walkthroughs The course starts with essential terminology, explaining vector embeddings and the mechanics of semantic search, before moving into agentic design patterns and orchestration strategies. This course is designed for software developers, data enthusiasts, and tech professionals new to AI orchestration, requiring no prior experience with vector databases. Start reading today to build smarter, context-aware AI applications.