“The cool kids in AI are using RAG, or Retrieval-Augmented Generation, to include helpful information in LLM prompts. While building a RAG pipeline can be complex, recent improvements in data warehouse providers like BigQuery offer features to simplify the process. By using text embeddings, search functionality, and vector indexes within BigQuery, you can eliminate the need for multiple systems like OpenAI and Pinecone, making your pipeline more efficient. It’s crucial to have a solid data infrastructure before diving into RAG. This post walks you through the steps of building a successful RAG pipeline, from hypothesis testing to utilizing text embeddings effectively.”
https://www.rainforestqa.com/blog/data-warehouse-can-rag