RAGe: A Retrieval-Augmented Generation Evaluation Framework

Larissa Guder; João Pedro de Moura; Arthur Accorsi; Gustavo Losch do Amaral; Maurício Cecílio Magnaguagno; Felipe Meneguzzi; Marcio Sorraglia Pinho; Dalvan Griebler

arXiv · 2605.27445

RAGe: A Retrieval-Augmented Generation Evaluation Framework

Larissa Guder·João Pedro de Moura·Arthur Accorsi·Gustavo Losch do Amaral·Maurício Cecílio Magnaguagno·Felipe Meneguzzi·Marcio Sorraglia Pinho·Dalvan Griebler

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Abstract

Deploying Large Language Model (LLM) applications, particularly those relying on Retrieval-Augmented Generation (RAG), remains challenging due to high computational demands, outdated knowledge bases, and the need to manually select optimal pipeline components. In this work, we propose a modular framework for benchmarking and guiding the efficient development of RAG applications by focusing on resource telemetry and component recommendation, suggesting the best components for a domain-specific dataset. Our approach leverages core techniques in LLM applications, including document chunking, vector databases, embedding models, and retrievers, to evaluate trade-offs among accuracy, efficiency, and scalability. By directly correlating retrieval and generation quality with underlying hardware constraints, RAGe supports researchers to identify the most effective, domain-specific RAG setups for their specific operational needs, facilitating rapid prototyping even on consumer-grade hardware.

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Larissa Guder, João Pedro de Moura, Arthur Accorsi, Gustavo Losch do Amaral, Maurício Cecílio Magnaguagno, Felipe Meneguzzi, Marcio Sorraglia Pinho, Dalvan Griebler. 2026-05-23. RAGe: A Retrieval-Augmented Generation Evaluation Framework. https://arxiv.org/abs/2605.27445

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