Building RAG and Vector Search with BigQuery

Large language models are incredibly powerful, but they often struggle with hallucinations and lack access to your private data. Integrating your enterprise data with AI requires a robust, scalable data warehouse solution. In this text-only course, you will learn to build semantic search and Retrieval-Augmented Generation (RAG) workflows directly inside BigQuery. You will discover how to turn unstructured text into vector embeddings and retrieve relevant context without moving your data out of your analytics environment. What you'll learn: - Understand the foundational concepts of vector embeddings, semantic search, and RAG architectures. - Generate text embeddings using built-in BigQuery machine learning functions. - Perform efficient vector searches to locate highly relevant data points quickly. - Implement RAG patterns to ground large language models with your own datasets. - Minimize AI hallucinations by designing precise context-retrieval pipelines. - Apply modern best practices for managing vector data and basic prompt engineering. This text-based guide begins with essential database and AI terminology before walking you through practical SQL-based vector operations. Designed specifically for beginners, this course requires no prior machine learning experience. Start reading today to bridge the gap between your analytical data and generative AI.