Vector Search and RAG with BigQuery for Beginners

Large language models often hallucinate or lack access to your private, enterprise data. By leveraging BigQuery for vector search and Retrieval-Augmented Generation (RAG), you can ground your AI applications in accurate, real-time data directly within your data warehouse. This text-only course guides you step-by-step through the process of generating embeddings, performing semantic vector searches, and building robust RAG pipelines. You will transition from writing standard SQL queries to constructing intelligent, context-aware data workflows that fuel modern AI systems. What you'll learn: - Understand foundational concepts of vector embeddings, semantic search, and RAG architectures. - Generate text embeddings using built-in BigQuery machine learning functions. - Configure and execute vector search queries to find semantically similar data points. - Build end-to-end RAG workflows to feed highly relevant context to language models. - Apply best practices for indexing, performance optimization, and mitigating AI hallucinations. This course begins with essential terminology, basic concepts, and foundational definitions of vector spaces before guiding you through hands-on SQL implementations of vector search and RAG integration patterns. It is designed for data analysts, database administrators, and aspiring AI engineers who are new to vector databases and want to leverage their existing SQL knowledge. No prior machine learning experience is required. Start reading today to unlock the power of semantic search in your data warehouse.