RAG-Stack: Optimizing Quality-Performance Trade-offs in Retrieval-Augmented Generation
RAG-Stack is a newly introduced framework designed to enhance the balance between answer quality and performance in retrieval-augmented generation (RAG) systems. RAG integrates large language model (LLM) generation with data retrieved from databases, making it popular for knowledge-heavy tasks. However, the various configuration options in contemporary RAG systems—like retrieval indexes and model choices—create different quality-performance trade-offs, complicating the selection of the best setup for specific uses. The framework, detailed in an arXiv paper (2608.03487), identifies quality-performance Pareto frontiers across various RAG applications. It comprises three parts: RAG-PE, RAG-IR, and RAG-CM, addressing a significant challenge in RAG system deployment.
Key facts
- RAG-Stack is a framework for discovering quality-performance Pareto frontiers in RAG systems.
- It consists of RAG-PE, RAG-IR, and RAG-CM components.
- RAG-PE is an iterative design-space exploration algorithm.
- RAG-IR is a workload abstraction for diverse RAG algorithms.
- RAG-CM is a performance model that predicts optimal deployment.
- The paper is available on arXiv with ID 2608.03487.
- The announcement type is 'cross', indicating possible conference submission.
- RAG augments LLM generation with retrieved information from databases.
Entities
Institutions
- arXiv