Surprise, your data warehouse can RAG

In a blog post, Maciej Gryka discusses the complexities of building a Retrieval-Augmented Generation (RAG) pipeline for AI systems. RAG involves including helpful information in language model prompts to improve results. Gryka highlights the importance of having a solid data infrastructure, like a data warehouse, before implementing RAG effectively. He explains the steps involved in building a RAG pipeline, focusing on using text embeddings for data retrieval. Gryka shares insights from their experience in implementing a RAG pipeline using BigQuery and leveraging built-in features to simplify the process. The post emphasizes the need to measure the success of RAG implementations and offers practical advice on testing hypotheses and improving response quality. Gryka also touches on the challenges of finding and retrieving relevant records, showcasing their approach to aggregating and processing data for RAG. Overall, the post provides a comprehensive guide for organizations considering implementing RAG in their AI systems, stressing the importance of a robust data infrastructure for successful integration.