Vector Search and RAG Solutions in BigQuery

As organizations increasingly adopt large language models, mitigating AI hallucinations and grounding responses in real enterprise data has become a critical challenge. BigQuery provides powerful, native capabilities to handle embeddings and search enterprise data efficiently without complex external infrastructure. This text-based course guides you through the foundational concepts of vector databases and semantic search, showing you how to turn raw data into actionable knowledge. You will learn how to generate embeddings, run vector searches, and connect these pieces to build robust Retrieval-Augmented Generation (RAG) pipelines directly inside your data warehouse. What you'll learn: Understand the fundamental concepts of vector embeddings, semantic search, and RAG architectures; Generate text and data embeddings using BigQuery's built-in machine learning capabilities; Perform efficient vector searches to find semantically similar data within massive datasets; Configure RAG pipelines to ground large language models with your own enterprise data; Apply best practices for structuring data and optimizing vector queries for real-world performance. The course starts with key terminology, basic concepts, and foundational definitions of vector spaces before moving into step-by-step written explanations of SQL-based machine learning functions. You will progress from writing basic vector queries to structuring full workflows that connect your data warehouse to generative AI outputs. This program is designed for data analysts, developers, and aspiring data engineers who want to integrate modern AI search capabilities into their existing workflows, with no prior machine learning experience required. Start reading today to unlock the power of semantic search and RAG within your data warehouse.