Building RAG Systems and Vector Search in BigQuery

Large language models often hallucinate or lack access to your private data. To build reliable AI applications, you need to ground your models using your own structured and unstructured datasets. In this text-only course, you will learn how to leverage BigQuery as a powerful engine for vector search and Retrieval-Augmented Generation (RAG). By understanding how to generate embeddings, store them, and query them efficiently, you will transform raw data into highly relevant context for generative AI models. What you will learn: Understand the foundational concepts of embeddings, vector spaces, and semantic search; Generate text embeddings directly within BigQuery using modern SQL functions; Perform efficient vector searches to locate relevant context matching user queries; Build a complete RAG workflow to supply LLMs with accurate, real-time data; Apply prompt engineering basics to combine retrieved context with user prompts; Evaluate RAG output quality to minimize AI hallucinations and ensure groundedness. You will start with key terminology and the architecture of vector databases before moving into practical SQL-based vector operations. Through clear written explanations and step-by-step code snippets, you will progress from basic embedding generation to a fully functional RAG pipeline. This course is designed for data analysts, developers, and AI enthusiasts who are new to vector databases and RAG. No prior machine learning experience is required, though a basic familiarity with SQL is helpful. Start reading today to unlock the power of semantic search and modern AI retrieval workflows.