Implementing Retrieval-Augmented Generation (RAG) with SQL

Combining the analytical power of relational databases with the intelligence of large language models is the key to building next-generation AI applications. This text-based course guides you through the fundamentals of Retrieval-Augmented Generation (RAG) using SQL. You will transition from writing standard database queries to designing intelligent systems that retrieve relevant data context and feed it directly into language models. You will learn how to structure database tables for AI, generate context-rich prompts, and handle model outputs safely and effectively. What you'll learn: Understand the foundational concepts of RAG and how relational databases serve as knowledge bases; Prepare and structure database context to optimize large language model processing; Write SQL queries to retrieve highly relevant data for dynamic prompt construction; Implement vector search concepts and modern embedding strategies within relational database systems; Construct clean, context-augmented prompts that guide AI models to accurate answers; Process and validate model responses to ensure reliability and security in your applications. The course starts with essential terminology and the core mechanics of RAG before moving into practical SQL patterns, prompt-building workflows, and response processing. You will work through written explanations and step-by-step query designs to solidify your understanding. This course is designed for database developers, software engineers, and beginners curious about AI integration who have a basic understanding of SQL. No prior experience with machine learning or AI modeling is required. Start reading today to unlock the potential of AI-driven database applications.