Vector Search and RAG with BigQuery: A Practical Guide

Modern artificial intelligence relies on connecting large language models with your organization's private data. BigQuery makes this powerful integration accessible using the SQL skills you already possess. In this text-based course, you will learn how to build Retrieval-Augmented Generation (RAG) systems and perform vector searches directly within your data warehouse. You will move from understanding foundational AI concepts to executing practical SQL queries that generate embeddings and retrieve semantically similar data. Through clear, written explanations, you will learn to: 1. Understand the foundational concepts of vector embeddings, semantic search, and RAG architectures. 2. Generate and store vector embeddings directly inside BigQuery using SQL. 3. Perform efficient vector search queries to find semantically related information. 4. Connect retrieved data context to large language models for accurate RAG responses. 5. Apply prompt engineering basics to format retrieved context for optimal model performance. 6. Practice writing clean, modern SQL queries for AI-driven data tasks. The course begins with essential terminology, introducing how computers represent meaning through vectors. From there, you will progress through structured text lessons with code snippets, learning to configure vector search indexes and build a fully functional RAG pipeline. This program is designed for data analysts, developers, and database administrators who are new to AI and vector databases, with no prior machine learning experience required. Start reading today to unlock the power of semantic search in your data warehouse.