@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://preview.aclanthology.org/ingest-acl/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."
}