  This paper introduces uRAG--a framework with a unified retrieval engine that
serves multiple downstream retrieval-augmented generation (RAG) systems. Each
RAG system consumes the retrieval results for a unique purpose, such as
open-domain question answering, fact verification, entity linking, and relation
extraction. We introduce a generic training guideline that standardizes the
communication between the search engine and the downstream RAG systems that
engage in optimizing the retrieval model. This lays the groundwork for us to
build a large-scale experimentation ecosystem consisting of 18 RAG systems that
engage in training and 18 unknown RAG systems that use the uRAG as the new
users of the search engine. Using this experimentation ecosystem, we answer a
number of fundamental research questions that improve our understanding of
promises and challenges in developing search engines for machines.
