RAGe: A Retrieval-Augmented Generation Evaluation Framework
Fuente:
arXiv
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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866910261525348352 |
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| author | Guder, Larissa de Moura, João Pedro Accorsi, Arthur Amaral, Gustavo Losch do Magnaguagno, Maurício Cecílio Meneguzzi, Felipe Pinho, Marcio Sorraglia Griebler, Dalvan |
| author_facet | Guder, Larissa de Moura, João Pedro Accorsi, Arthur Amaral, Gustavo Losch do Magnaguagno, Maurício Cecílio Meneguzzi, Felipe Pinho, Marcio Sorraglia Griebler, Dalvan |
| contents | 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those relying on Retrieval-Augmented Generation (RAG), remains challenging due to high computational demands, outdated knowledge bases, and the need to manually select optimal pipeline components.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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_27445 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | RAGe: A Retrieval-Augmented Generation Evaluation Framework Guder, Larissa de Moura, João Pedro Accorsi, Arthur Amaral, Gustavo Losch do Magnaguagno, Maurício Cecílio Meneguzzi, Felipe Pinho, Marcio Sorraglia Griebler, Dalvan Information Retrieval Artificial Intelligence 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those relying on Retrieval-Augmented Generation (RAG), remains challenging due to high computational demands, outdated knowledge bases, and the need to manually select optimal pipeline components.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. |
| title | RAGe: A Retrieval-Augmented Generation Evaluation Framework |
| topic | Information Retrieval Artificial Intelligence |
| url | https://arxiv.org/abs/2605.27445 |