Generative AI Agents with Azure Database for PostgreSQL

Organizations are increasingly leveraging their operational data to power intelligent applications. This text-based course guides you through the process of building autonomous generative AI agents using Azure Database for PostgreSQL. You will start with the fundamental concepts of vector databases and semantic search, then transition into designing and implementing robust Retrieval-Augmented Generation (RAG) architectures. By the end of this course, you will understand how to securely store vector embeddings, construct agentic workflows, and evaluate agent performance using real-world data scenarios. What you'll learn: - Understand the foundational principles of generative AI agents and vector embeddings. - Configure Azure Database for PostgreSQL with the pgvector extension for efficient similarity search. - Implement Retrieval-Augmented Generation (RAG) patterns to connect LLMs to your database. - Build autonomous agent workflows that retrieve, process, and act on database records. - Apply modern prompt engineering techniques to guide agent behavior and tool usage. - Evaluate agent responses for accuracy, relevance, and safety. The course starts with essential terminology and database configuration before moving step-by-step through vector operations, LLM integration, and agent deployment strategies. You will learn through clear written explanations, structured SQL and programming examples, and practical architectural walk-throughs. This course is designed for database professionals, backend developers, and aspiring AI engineers who are new to generative AI. No prior experience with artificial intelligence or machine learning is required, though a basic familiarity with SQL is recommended. Start reading today to unlock the potential of AI-driven database applications.