Building RAG Applications with Azure Databricks

Modern AI applications require accurate, context-aware responses based on private organizational data. This written course guides you through implementing Retrieval Augmented Generation (RAG) architectures to ground large language models in your specific domain knowledge. By reading and working through the practical code examples, you will transition from understanding basic generative AI concepts to deploying a fully functional retrieval pipeline. You will learn to prepare unstructured data, index it for fast retrieval, and integrate it seamlessly with language models. What you'll learn: - Understand the foundational architecture of Retrieval Augmented Generation and vector databases. - Configure Azure Databricks environments for managing vector indexes and embeddings. - Process and chunk unstructured text data to optimize retrieval relevance. - Implement semantic search queries using Databricks Vector Search. - Connect retrieved context to language models to generate accurate, grounded responses. - Track and evaluate RAG system performance using MLflow integration. The course begins with core definitions and architectural patterns before moving into step-by-step written tutorials on data preparation, vector indexing, and model integration. You will study clear code snippets and conceptual explanations designed to build your skills systematically. This course is designed for data engineers, software developers, and aspiring AI practitioners who are new to RAG architectures, with no prior Databricks experience required. Start reading today to build smarter, data-driven AI applications on Databricks.