Vector Search and RAG Pipelines in BigQuery

Generative AI models are incredibly powerful, but they often struggle with hallucinations and lack access to your private organizational data. This text-based course guides you through the process of building Retrieval-Augmented Generation (RAG) pipelines directly inside BigQuery. You will start with the essential terminology of vector databases and semantic search before moving on to practical SQL-based workflows. By the end of this course, you will know how to generate vector embeddings, perform similarity searches, and ground large language models using your own enterprise data. What you'll learn: 1. Understand the foundational concepts of vector spaces, embeddings, and semantic search. 2. Generate and manage vector embeddings directly inside BigQuery tables. 3. Perform high-performance vector searches using SQL queries. 4. Build end-to-end Retrieval-Augmented Generation pipelines to reduce model hallucinations. 5. Apply basic prompt engineering principles to ground generative AI models with retrieved context. 6. Evaluate the quality and accuracy of your search results and RAG outputs. The journey begins with foundational definitions of vector math and semantic retrieval, followed by step-by-step written tutorials demonstrating how to configure BigQuery for machine learning tasks. You will then practice writing queries to generate embeddings, execute vector searches, and connect retrieved data to generative models. This course is designed for data analysts, database developers, and aspiring AI engineers who want to build RAG systems using SQL. No prior experience with machine learning or complex vector databases is required, though a basic understanding of SQL is helpful. Start reading today to unlock the power of semantic search and RAG inside your data warehouse.