Building AI Copilots with Cosmos DB for MongoDB and Azure OpenAI

Integrating artificial intelligence into modern applications is key to delivering engaging user experiences. Building a custom AI copilot allows your applications to understand context, search complex datasets, and provide natural, human-like responses. This text-based course guides you through the foundational steps of designing and deploying an AI copilot. You will learn how to leverage vCore-based Cosmos DB for MongoDB to store and query high-dimensional vector embeddings alongside your application data, and connect it with Azure OpenAI to generate context-aware responses. What you'll learn: - Understand the core concepts of vector databases, embeddings, and Retrieval-Augmented Generation (RAG) patterns - Configure a vCore-based Cosmos DB for MongoDB database for storing application and vector data - Generate vector embeddings using Azure OpenAI models to represent textual information - Perform vector similarity searches to retrieve highly relevant context from your database - Build a structured prompt pipeline to feed retrieved data into large language models - Implement conversational memory to maintain context across multi-turn interactions The course begins with essential database terminology and the mechanics of vector search. From there, you will explore written explanations and code examples that demonstrate how to ingest data, generate embeddings, execute semantic queries, and orchestrate the entire copilot workflow. This course is designed for software developers, database enthusiasts, and technology professionals who are new to AI integration. No prior experience with artificial intelligence or vector databases is required. Start reading today to learn how to build intelligent, context-aware applications from scratch.