Building AI Applications with Azure Database for PostgreSQL

Modern applications require more than just standard relational queries; they need intelligent, context-aware capabilities powered by artificial intelligence. Azure Database for PostgreSQL provides a robust, scalable foundation for integrating machine learning and large language models directly with your relational data.\n\nThis text-based course guides you through the process of transforming a standard database into an AI-ready database. You will start with the fundamental concepts of vector databases and semantic search, and progress to implementing practical AI patterns without needing complex external infrastructure.\n\nWhat you'll learn:\n- Understand the foundational concepts of vector embeddings and relational databases\n- Configure the pgvector extension within Azure Database for PostgreSQL to store and query high-dimensional vectors\n- Build semantic search queries to retrieve contextually relevant data instead of exact keyword matches\n- Implement Retrieval-Augmented Generation (RAG) patterns to connect your database with large language models\n- Apply performance tuning practices for indexing vector data to ensure fast, scalable AI queries\n- Practice designing database schemas that support both traditional relational data and modern AI features\n\nThe course begins with core definitions and terminology, ensuring you have a solid grasp of vector embeddings. You will then progress through clear written explanations and practical SQL code snippets that demonstrate how to write, optimize, and integrate AI queries in PostgreSQL.\n\nThis course is designed for backend developers, database administrators, and aspiring AI engineers who are new to vector databases. No prior experience with artificial intelligence is required, though a basic understanding of SQL is recommended.\n\nStart reading today to unlock the potential of intelligent databases and build smarter applications.