Designing Production RAG Applications: Planning and Scoping

Moving an AI application from a prototype to a stable production environment requires careful planning and structured architecture. This text-based course guides you through the essential steps of scoping and designing robust Retrieval-Augmented Generation (RAG) systems. You will transition from building simple prompts to architecting production-grade RAG pipelines, gaining a clear framework for selecting the right data ingestion strategies, managing vector databases, and establishing reliable evaluation metrics. What you'll learn: Understand foundational RAG architecture, key terminology, and the core stages of the LLMOps lifecycle; Scope project requirements, user expectations, and resource constraints for your AI application; Design efficient data engineering pipelines for document parsing, chunking, and metadata tagging; Select and configure vector databases and embedding models tailored to your specific domain; Establish robust evaluation frameworks to measure retrieval accuracy and generation quality; Plan for continuous monitoring, observability, and cost management in production environments. The course begins with essential definitions and foundational RAG concepts before moving systematically through data preparation, system design, and evaluation strategies. Through structured written explanations and practical architectural blueprints, you will learn how to plan an enterprise-ready AI system. This course is designed for software engineers, product managers, and data professionals who are new to RAG architectures and want to learn the planning phase without complex prerequisites. Start planning your production-grade RAG application today.