Building a Production RAG System: From Prototype to Deployment

Developing effective applications with large language models requires grounding them in domain-specific knowledge. Retrieval-Augmented Generation (RAG) is the definitive pattern for making LLMs accurate, context-aware, and up-to-date. Upon completing this course, you will move beyond simple prompt engineering to build scalable, tested AI applications that provide accurate, context-aware responses by integrating external data sources effectively. You will learn the entire pipeline necessary to take an RAG concept from a basic script to a fully deployed production service. What you'll learn: * Understand the core architecture of Retrieval-Augmented Generation (RAG) systems and why they are essential for enterprise AI. * Configure effective data indexing and retrieval strategies using modern vector databases and embedding techniques. * Build a robust backend API for your RAG system using the high-performance FastAPI framework. * Practice writing unit tests and implementing RAG-specific evaluation metrics for quality assurance. * Apply modern containerization and deployment fundamentals to move your RAG prototype into a production environment. The course begins with foundational concepts of LLMs and vector embeddings, then guides you step-by-step through building the data ingestion pipeline, creating the API layer, and finalizing the application for deployment. This course is designed for beginner to intermediate developers and data analysts who are familiar with basic Python programming and want to specialize in production AI systems. No prior experience with vector databases or deployment tools is required. Start building the next generation of intelligent applications today.