Optimizing Retrieval-Augmented Generation for E-Commerce How-To Assistance

Gilad Fuchs; Leonid Ekimov; Fei Dong; Jiahong Xie; Wei Liu; Maxim Manco; Alexander Nus

Optimizing Retrieval-Augmented Generation for E-Commerce How-To Assistance

Gilad Fuchs, Leonid Ekimov, Fei Dong, Jiahong Xie, Wei Liu, Maxim Manco, Alexander Nus

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Abstract

Conversational AI is increasingly used at eBay to deliver personalized customer support. We present a production RAG-based How-To Assistant that answers support and how-to queries by grounding responses in a proprietary knowledge base. We study three factors that drive quality: (1) document chunking and contextualization for indexing, (2) query refinement methods, and (3) automatic LLM-based evaluation for rapid iteration and reliable measurement. We also describe the end-to-end system workflow - from offline indexing to real-time serving and report deployment metrics, offering practical guidance for building scalable, high-precision RAG assistants in commercial support settings.

Anthology ID:: 2026.acl-industry.31
Volume:: Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026)
Month:: July
Year:: 2026
Address:: San Diego, California, USA
Editors:: Yunyao Li, Georg Rehm, Mei Tu
Venue:: ACL
SIG:
Publisher:: Association for Computational Linguistics
Note:
Pages:: 459–466
Language:
URL:: https://aclanthology.org/2026.acl-industry.31/
DOI:
Bibkey:
Cite (ACL):: Gilad Fuchs, Leonid Ekimov, Fei Dong, Jiahong Xie, Wei Liu, Maxim Manco, and Alexander Nus. 2026. Optimizing Retrieval-Augmented Generation for E-Commerce How-To Assistance. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026), pages 459–466, San Diego, California, USA. Association for Computational Linguistics.
Cite (Informal):: Optimizing Retrieval-Augmented Generation for E-Commerce How-To Assistance (Fuchs et al., ACL 2026)
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PDF:: https://aclanthology.org/2026.acl-industry.31.pdf

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@inproceedings{fuchs-etal-2026-optimizing,
    title = "Optimizing Retrieval-Augmented Generation for {E}-Commerce How-To Assistance",
    author = "Fuchs, Gilad  and
      Ekimov, Leonid  and
      Dong, Fei  and
      Xie, Jiahong  and
      Liu, Wei  and
      Manco, Maxim  and
      Nus, Alexander",
    editor = "Li, Yunyao  and
      Rehm, Georg  and
      Tu, Mei",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics ({ACL} 2026)",
    month = jul,
    year = "2026",
    address = "San Diego, California, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2026.acl-industry.31/",
    pages = "459--466",
    ISBN = "979-8-89176-394-4",
    abstract = "Conversational AI is increasingly used at eBay to deliver personalized customer support. We present a production RAG-based How-To Assistant that answers support and how-to queries by grounding responses in a proprietary knowledge base. We study three factors that drive quality: (1) document chunking and contextualization for indexing, (2) query refinement methods, and (3) automatic LLM-based evaluation for rapid iteration and reliable measurement. We also describe the end-to-end system workflow - from offline indexing to real-time serving and report deployment metrics, offering practical guidance for building scalable, high-precision RAG assistants in commercial support settings."
}

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%0 Conference Proceedings
%T Optimizing Retrieval-Augmented Generation for E-Commerce How-To Assistance
%A Fuchs, Gilad
%A Ekimov, Leonid
%A Dong, Fei
%A Xie, Jiahong
%A Liu, Wei
%A Manco, Maxim
%A Nus, Alexander
%Y Li, Yunyao
%Y Rehm, Georg
%Y Tu, Mei
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, USA
%@ 979-8-89176-394-4
%F fuchs-etal-2026-optimizing
%X Conversational AI is increasingly used at eBay to deliver personalized customer support. We present a production RAG-based How-To Assistant that answers support and how-to queries by grounding responses in a proprietary knowledge base. We study three factors that drive quality: (1) document chunking and contextualization for indexing, (2) query refinement methods, and (3) automatic LLM-based evaluation for rapid iteration and reliable measurement. We also describe the end-to-end system workflow - from offline indexing to real-time serving and report deployment metrics, offering practical guidance for building scalable, high-precision RAG assistants in commercial support settings.
%U https://aclanthology.org/2026.acl-industry.31/
%P 459-466

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Markdown (Informal)

[Optimizing Retrieval-Augmented Generation for E-Commerce How-To Assistance](https://aclanthology.org/2026.acl-industry.31/) (Fuchs et al., ACL 2026)

Optimizing Retrieval-Augmented Generation for E-Commerce How-To Assistance (Fuchs et al., ACL 2026)

ACL

Gilad Fuchs, Leonid Ekimov, Fei Dong, Jiahong Xie, Wei Liu, Maxim Manco, and Alexander Nus. 2026. Optimizing Retrieval-Augmented Generation for E-Commerce How-To Assistance. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026), pages 459–466, San Diego, California, USA. Association for Computational Linguistics.