Building RAG and Vector Search Solutions in BigQuery

Harnessing the power of large language models requires grounding them in your own enterprise data. This text-based course guides you through using BigQuery to build robust Retrieval-Augmented Generation (RAG) systems that minimize AI hallucinations and deliver precise, context-aware results. You will transition from understanding basic data storage to implementing advanced semantic search pipelines. By learning how to generate embeddings and execute vector search queries, you will gain the skills needed to connect your data to modern generative AI models. What you'll learn: Understand the foundational concepts of vector embeddings and semantic similarity; Generate text embeddings directly within BigQuery using built-in machine learning capabilities; Configure and execute vector search queries to retrieve highly relevant context; Build a complete RAG workflow that combines retrieved data with prompts for language models; Apply best practices for structuring data and evaluating retrieval accuracy. The course begins with core definitions of vector databases and semantic search before diving into practical SQL-based implementations. You will progress through step-by-step written explanations that cover embedding generation, vector indexing, and pipeline integration. This course is designed for data analysts, database administrators, and aspiring AI developers who are new to vector databases and want to leverage their existing SQL knowledge. No prior machine learning experience is required. Start reading today to unlock the potential of semantic search and RAG within your data warehouse.