Building an AI Copilot with Cosmos DB and OpenAI

Modern applications increasingly rely on intelligent, context-aware AI assistants to help users navigate data and automate tasks. Building these systems requires a solid understanding of how to connect large language models with your own database. This text-only course guides you through the process of building your own AI copilot. You will transition from understanding basic database concepts to integrating vector databases and implementing Retrieval-Augmented Generation (RAG) to ground your AI assistant in real-world data. What you'll learn: 1. Understand the fundamentals of vector databases and semantic search. 2. Configure Cosmos DB for MongoDB to store and query high-dimensional vector embeddings. 3. Apply prompt engineering basics to guide your AI copilot's behavior and responses. 4. Implement Retrieval-Augmented Generation (RAG) patterns to connect LLMs with your database. 5. Build a functional backend architecture for a context-aware AI assistant. 6. Practice secure data handling and query optimization for AI-driven applications. The course starts with essential definitions of vector search and database design, then progresses step-by-step through configuring your database, generating embeddings, and constructing the core logic of your AI copilot. This training is designed for software developers and database beginners who want to enter the world of AI engineering, and no prior experience with AI models or vector databases is required. Start reading today to build your own intelligent database-driven assistant.