Generative AI is everywhere today, but few understand the fundamental concepts it's built upon. "The Hidden Foundation of GenAI" is a starting point for those who want to truly understand what lies behind LLM, vector search, and semantic understanding. This course is specifically designed for data engineers and focuses on embeddings—one of the most important (and most misinterpreted) building blocks of any GenAI system.
Instead of overloading with mathematical theory, we provide practical insights: how text is converted into vectors, how similarity is calculated, and how this underlies scenarios like semantic search and Retrieval-Augmented Generation (RAG). You will work with an interactive Embedding Playground, analyze Python examples, and gain the confidence to use vector search in your own projects.
This course opens a series of sessions on GenAI at the Academy. In the following modules, you will continue exploring semantic search, vector databases, and complete your journey with a full-fledged project—implementing a GenAI pipeline with RAG.
