Designing Scalable Retrieval-Augmented Generation (RAG) Systems

As generative AI applications grow, standard language models often struggle with outdated knowledge and hallucinations. Retrieval-Augmented Generation (RAG) solves this by connecting models to external data sources, but scaling these systems for real-time production requires careful architectural planning. This course guides you through the foundational principles of building scalable, reliable, and high-performance RAG systems. You will transition from understanding basic retrieval concepts to designing robust architectures that can handle large datasets and concurrent user queries. What you'll learn: Understand the core components of RAG, including document ingestion, embedding generation, and prompt construction; Compare and configure vector databases and indexing strategies for high-speed retrieval; Design scalable system architectures that handle resource estimation, caching, and query load balancing; Apply modern retrieval patterns such as hybrid search, query rewriting, and reranking to improve accuracy; Implement basic monitoring, evaluation metrics, and observability practices for production RAG pipelines. You will start with essential terminology and foundational definitions before progressing to system architecture patterns, data pipeline designs, and optimization strategies. The material is presented through clear, structured text and practical design scenarios that you can read and apply at your own pace. This course is designed for software engineers, system architects, and technical beginners who want to build production-ready AI applications. No prior experience with system design or machine learning operations is required. Start reading today to master the architectural patterns behind modern, scalable AI systems.