Building Vector Search and RAG Solutions in BigQuery

As generative AI models become central to modern applications, grounding them with your own enterprise data is essential to prevent hallucinations. BigQuery provides powerful, built-in capabilities to generate embeddings and run vector searches directly where your data lives. By completing this text-based course, you will learn how to build a complete Retrieval-Augmented Generation (RAG) pipeline using BigQuery. You will transition from understanding basic database concepts to structuring data for AI, generating vector embeddings, and executing semantic searches to feed context into large language models. What you will learn: - Understand the foundational concepts of vector embeddings, semantic search, and RAG architecture. - Generate text embeddings directly within BigQuery using built-in functions. - Perform efficient vector distance searches to find highly relevant context. - Design robust RAG workflows that ground language models and mitigate AI hallucinations. - Apply data preparation best practices to ensure high-quality vector search results. You will start with key terminology and foundational definitions before moving through written explanations of SQL queries and vector configurations. This course is perfect for beginners, data analysts, and developers looking to expand their skills into AI databases, with no prior machine learning experience required. Start reading today to unlock the power of semantic search in your data warehouse.