What you'll learn

Engineers often struggle with how to represent their data as vectors, choose and operate vector stores, design knowledge graphs, and build robust pipelines that keep everything up to date and testable at scale. In this course, GenAI Data and Knowledge Layer, you’ll gain the ability to design and implement production-ready data and knowledge layers that power reliable GenAI applications. First, you’ll explore how embeddings, vector stores, and chunking strategies turn raw data into effective semantic representations for retrieval-augmented generation. Next, you’ll discover how to architect and operate vector databases, integrate knowledge graphs, and orchestrate end-to-end pipelines that continuously ingest, transform, and index data. Finally, you’ll learn how to apply advanced preprocessing and synthetic data generation techniques to improve retrieval quality, robustness, and test coverage of your GenAI systems. When you’re finished with this course, you’ll have the skills and knowledge of modern GenAI data and knowledge layers needed to confidently build and maintain scalable, production-grade GenAI systems grounded in your own data.