Building RAG Systems and Vector Search in BigQuery

As generative AI models become central to modern applications, grounding them with your own enterprise data is crucial to prevent hallucinations. BigQuery offers powerful, built-in capabilities to generate embeddings and run vector searches directly where your data lives. In this text-based course, you will transition from understanding basic data warehousing to implementing fully functional Retrieval-Augmented Generation (RAG) pipelines. You will gain the skills to store high-dimensional vectors, execute similarity queries, and connect large language models to your structured datasets. What you'll learn: Understand the foundational concepts of vector embeddings and semantic search; Generate text embeddings using SQL queries directly inside BigQuery; Configure and optimize vector indexes to perform fast similarity searches at scale; Build robust RAG architectures to feed contextually relevant data to language models; Apply chunking and preprocessing strategies to prepare raw text for embedding generation; Mitigate model hallucinations by grounding LLM responses with verified warehouse data. You will start with the core terminology of vector databases and semantic representation before moving on to practical SQL-based implementations. Through structured explanations and clear code examples, you will learn how to orchestrate a complete RAG workflow from raw data ingestion to generated output. This course is designed for data analysts, database administrators, and aspiring AI engineers who are new to vector search and RAG. No prior machine learning experience is required; a basic familiarity with SQL is all you need to begin. Start reading today to unlock the power of semantic search inside your data warehouse.