Building RAG and Vector Search Solutions in BigQuery

As artificial intelligence transforms how we interact with data, connecting large language models to your enterprise data store is more critical than ever. Learn how to bridge this gap using the analytical power of BigQuery. This text-based course guides you through the core concepts of vector databases, semantic search, and Retrieval-Augmented Generation (RAG). You will understand how to represent unstructured data as vector embeddings and search them efficiently using standard database techniques. What you'll learn: - Understand the foundational concepts of vector embeddings and semantic similarity - Generate and manage text embeddings directly inside BigQuery - Perform efficient vector searches using SQL queries to find related information - Configure basic Retrieval-Augmented Generation (RAG) architectures to ground AI models - Apply best practices for optimizing vector query performance and managing data pipelines The course begins with essential terminology and the theory behind vector spaces, then moves step-by-step through writing SQL to generate embeddings, search vectors, and connect retrieved context to language models. It is designed for data analysts, developers, and aspiring AI engineers who are new to vector databases and want a clear, text-based introduction to modern search architectures without complex setup. Start reading today to unlock the power of semantic search in your data workflows.