Vector Search and RAG in BigQuery for Beginners

Connecting large language models to your enterprise data is key to building intelligent applications, but moving data between systems introduces complexity and security risks. This text-based course shows you how to generate embeddings, run vector searches, and implement Retrieval-Augmented Generation (RAG) directly within BigQuery. You will transition from traditional keyword matching to semantic search, learning how to leverage machine learning capabilities using familiar SQL workflows. By understanding the foundational concepts of vector spaces and practical query patterns, you will be able to enrich your data warehouse with modern AI capabilities. What you'll learn: Understand the core concepts of vector embeddings and semantic search; Generate text embeddings using SQL functions within BigQuery; Configure vector indexes to optimize search performance on large datasets; Build Retrieval-Augmented Generation (RAG) pipelines to ground AI models in your data; Apply basic prompt engineering principles to format retrieved context; Practice writing clean SQL queries for modern AI and semantic search workflows. The course begins with essential terminology, explaining what embeddings are and how vector databases work conceptually. From there, you will read through step-by-step SQL implementations, learning to create indexes, run similarity searches, and architect a complete RAG workflow. This course is designed for data analysts, database administrators, and software developers who want to integrate AI search features into their existing data warehouse. No prior machine learning background is required, though a basic familiarity with SQL is helpful. Start reading now to master semantic search and RAG inside BigQuery.