Designing and Optimizing Generative AI Data Systems

As generative AI becomes a core part of modern technology, the demand for scalable, efficient, and reliable data infrastructure has never been higher. Building these systems requires a solid understanding of how data flows, stores, and retrieves to power large language models. In this course, you will transition from a traditional data perspective to designing robust infrastructure tailored for generative AI. You will learn how to structure data pipelines, optimize retrieval processes, and manage modern vector databases to ensure fast, accurate AI responses. What you'll learn: - Understand the foundational concepts of generative AI data architecture and storage requirements - Configure vector databases to store, index, and retrieve high-dimensional embeddings efficiently - Implement Retrieval-Augmented Generation patterns to connect models with external data sources - Optimize data pipelines for lower latency, higher throughput, and reduced computational costs - Apply data chunking and metadata strategies to improve search relevance and accuracy - Address modern data privacy, security, and governance standards within AI systems You will start with key terminology and foundational data concepts before moving step-by-step through pipeline design, vector indexing, and system optimization. Through clear written explanations and structured practice scenarios, you will build a solid blueprint for enterprise-grade AI data systems. This course is designed for beginning data engineers, software developers, and system architects looking to enter the AI space, with no prior generative AI infrastructure experience required. Start reading today to master the essential data foundations of the generative AI era.