Surprise, your data warehouse can RAG

In a blog post by Maciej Gryka, the concept of "Retrieval-Augmented Generation" (RAG) is discussed as a method to enhance AI systems by including helpful information in prompts. The post delves into building a RAG pipeline, highlighting the importance of measuring success and testing hypotheses to ensure improvements in results. The use of text embeddings for data retrieval is explained, emphasizing the steps involved in setting up a RAG pipeline and the considerations for choosing between embeddings and keyword search. The post also touches on the significance of having a robust data infrastructure, like a data warehouse, before implementing RAG effectively. Gryka shares insights on their experience with building a RAG pipeline using BigQuery and the benefits of leveraging built-in features of data warehouses for simplifying the process. The post provides a detailed walkthrough of their initial multi-system implementation and the transition to a BigQuery-only setup, showcasing the challenges and decisions involved in optimizing the RAG pipeline for efficient data retrieval.