Developing RAG Systems with Azure OpenAI and Azure AI Search

Connecting large language models to your proprietary business data is the key to building truly useful AI applications. This course guides you through the process of designing and deploying Retrieval-Augmented Generation (RAG) systems using the Azure ecosystem. By reading through this comprehensive guide, you will transition from understanding basic AI concepts to implementing sophisticated, production-ready retrieval architectures. You will learn how to prepare data, generate embeddings, and orchestrate search workflows that deliver highly accurate, context-aware AI responses. What you'll learn: - Understand the core principles of RAG, vector embeddings, and semantic search - Configure Azure AI Search to index, store, and retrieve unstructured documents - Implement vector database capabilities and hybrid search queries with CosmosDB - Explore GraphRAG architectures using Neo4j to analyze complex data relationships - Design multi-modal RAG systems that handle both text and image inputs - Integrate search components with language models inside Azure AI Studio This text-based course begins with essential terminology and foundational search concepts before moving into step-by-step implementation guides and architectural patterns. Through clear written explanations and practical code snippets, you will gain a deep, conceptual understanding of modern retrieval systems. This course is designed for software developers, data professionals, and AI enthusiasts who want to build custom data-connected AI solutions. No prior experience with RAG or Azure AI services is required. Start reading today to build secure, scalable retrieval systems for your AI applications.