Generative AI Applications with Azure Cosmos DB and Python

Modern applications require intelligent, context-aware capabilities driven by artificial intelligence. Integrating large language models with your own database allows you to build powerful, secure, and highly relevant AI experiences. In this course, you will learn how to design and build Generative AI applications using Python and Azure Cosmos DB for NoSQL. You will transition from understanding core database concepts to implementing advanced vector search and Retrieval-Augmented Generation (RAG) architectures. What you'll learn: - Understand the fundamentals of vector embeddings and semantic search. - Configure Azure Cosmos DB for NoSQL to store and query high-dimensional vector data. - Build Retrieval-Augmented Generation (RAG) workflows using Python. - Connect large language models to your database for context-rich AI responses. - Apply best practices for managing conversational memory and indexing strategies. The course starts with foundational concepts of NoSQL databases and vector embeddings before moving into practical Python integration. You will read through clear explanations and structured code snippets designed to show you how to connect database storage with AI orchestration. This course is designed for developers and database learners who are new to AI engineering. A basic familiarity with Python is recommended, but no prior experience with Azure Cosmos DB or Generative AI is required. Start reading today to build your first database-backed intelligent application.