@inproceedings{afzal-etal-2026-beyond-grid,
title = "Beyond Grid Search: Leveraging {B}ayesian Optimization for Accelerating {RAG} Pipeline Optimization",
author = "Afzal, Anum and
Zheng, Xueru and
Matthes, Florian",
editor = {Matusevych, Yevgen and
Eryi{\u{g}}it, G{\"u}l{\c{s}}en and
Aletras, Nikolaos},
booktitle = "Proceedings of the 19th Conference of the {E}uropean Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 5: Industry Track)",
month = mar,
year = "2026",
address = "Rabat, Morocco",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.eacl-industry.19/",
pages = "263--277",
ISBN = "979-8-89176-384-5",
abstract = "Finding optimal configurations for Retrieval-Augmented Generation (RAG) pipelines via grid search is computationally prohibitive, limiting real-world scalability. We investigate Bayesian Optimization (BO) as an efficient alternative, systematically comparing seven BO strategies combining four surrogate models and two multi-fidelity methods across FiQA, SciFact, and HotpotQA datasets. Our framework explores both global pipeline and local component-wise optimization, targeting final RAG performance and resource efficiency. Our results show that BO reduces optimization time by up to 84{\%} compared to grid search while maintaining comparable accuracy, with local optimization offering the most practical balance for deployment. Notably, performance gains plateau with larger evaluation budgets, suggesting that moderate resource investments suffice for effective RAG tuning. We provide actionable guidelines that empower industry practitioners to efficiently configure and deploy high-performing RAG systems under real-world constraints."
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<abstract>Finding optimal configurations for Retrieval-Augmented Generation (RAG) pipelines via grid search is computationally prohibitive, limiting real-world scalability. We investigate Bayesian Optimization (BO) as an efficient alternative, systematically comparing seven BO strategies combining four surrogate models and two multi-fidelity methods across FiQA, SciFact, and HotpotQA datasets. Our framework explores both global pipeline and local component-wise optimization, targeting final RAG performance and resource efficiency. Our results show that BO reduces optimization time by up to 84% compared to grid search while maintaining comparable accuracy, with local optimization offering the most practical balance for deployment. Notably, performance gains plateau with larger evaluation budgets, suggesting that moderate resource investments suffice for effective RAG tuning. We provide actionable guidelines that empower industry practitioners to efficiently configure and deploy high-performing RAG systems under real-world constraints.</abstract>
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%0 Conference Proceedings
%T Beyond Grid Search: Leveraging Bayesian Optimization for Accelerating RAG Pipeline Optimization
%A Afzal, Anum
%A Zheng, Xueru
%A Matthes, Florian
%Y Matusevych, Yevgen
%Y Eryiğit, Gülşen
%Y Aletras, Nikolaos
%S Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 5: Industry Track)
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%@ 979-8-89176-384-5
%F afzal-etal-2026-beyond-grid
%X Finding optimal configurations for Retrieval-Augmented Generation (RAG) pipelines via grid search is computationally prohibitive, limiting real-world scalability. We investigate Bayesian Optimization (BO) as an efficient alternative, systematically comparing seven BO strategies combining four surrogate models and two multi-fidelity methods across FiQA, SciFact, and HotpotQA datasets. Our framework explores both global pipeline and local component-wise optimization, targeting final RAG performance and resource efficiency. Our results show that BO reduces optimization time by up to 84% compared to grid search while maintaining comparable accuracy, with local optimization offering the most practical balance for deployment. Notably, performance gains plateau with larger evaluation budgets, suggesting that moderate resource investments suffice for effective RAG tuning. We provide actionable guidelines that empower industry practitioners to efficiently configure and deploy high-performing RAG systems under real-world constraints.
%U https://aclanthology.org/2026.eacl-industry.19/
%P 263-277
Markdown (Informal)
[Beyond Grid Search: Leveraging Bayesian Optimization for Accelerating RAG Pipeline Optimization](https://aclanthology.org/2026.eacl-industry.19/) (Afzal et al., EACL 2026)
ACL