@inproceedings{jucla-etal-2026-retrieval,
title = "Retrieval Enhancements for {RAG}: Insights from a Deployed Customer Support Chatbot",
author = "Jucl{\`a}, Daniel Gonz{\'a}lez and
Tuteja, Mohit and
Casademunt, Marcos Esteve and
Unnikrishnan, Keshav and
Usmani, Yasir and
Roshaan, Arvind",
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.13/",
pages = "169--180",
ISBN = "979-8-89176-384-5",
abstract = "Retrieval-Augmented Generation (RAG) systems depend critically on retrieval quality to enable accurate, contextually relevant LLM responses. While LLMs excel at synthesis, their RAG performance is bottlenecked by document relevance. We evaluate advanced retrieval techniques including embedding model comparison, Reciprocal Rank Fusion (RRF), embedding concatenation and list-wise and adaptive LLM-based re-ranking, demonstrating that zero-shot LLMs outperform traditional cross-encoders in identifying high-relevance passages. We also explore context-aware embeddings, diverse chunking strategies, and model fine-tuning. All methods are rigorously evaluated on a proprietary dataset powering our deployed production chatbot, with validation on three public benchmarks: FiQA, HotpotQA, and SciDocs. Results show consistent gains in Recall@10, closing the gap with Recall@50 and yielding actionable pipeline recommendations. By prioritizing retrieval enhancements, we significantly elevate downstream LLM response quality in real-world, customer-facing applications."
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%0 Conference Proceedings
%T Retrieval Enhancements for RAG: Insights from a Deployed Customer Support Chatbot
%A Juclà, Daniel González
%A Tuteja, Mohit
%A Casademunt, Marcos Esteve
%A Unnikrishnan, Keshav
%A Usmani, Yasir
%A Roshaan, Arvind
%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 jucla-etal-2026-retrieval
%X Retrieval-Augmented Generation (RAG) systems depend critically on retrieval quality to enable accurate, contextually relevant LLM responses. While LLMs excel at synthesis, their RAG performance is bottlenecked by document relevance. We evaluate advanced retrieval techniques including embedding model comparison, Reciprocal Rank Fusion (RRF), embedding concatenation and list-wise and adaptive LLM-based re-ranking, demonstrating that zero-shot LLMs outperform traditional cross-encoders in identifying high-relevance passages. We also explore context-aware embeddings, diverse chunking strategies, and model fine-tuning. All methods are rigorously evaluated on a proprietary dataset powering our deployed production chatbot, with validation on three public benchmarks: FiQA, HotpotQA, and SciDocs. Results show consistent gains in Recall@10, closing the gap with Recall@50 and yielding actionable pipeline recommendations. By prioritizing retrieval enhancements, we significantly elevate downstream LLM response quality in real-world, customer-facing applications.
%U https://aclanthology.org/2026.eacl-industry.13/
%P 169-180
Markdown (Informal)
[Retrieval Enhancements for RAG: Insights from a Deployed Customer Support Chatbot](https://aclanthology.org/2026.eacl-industry.13/) (Juclà et al., EACL 2026)
ACL
- Daniel González Juclà, Mohit Tuteja, Marcos Esteve Casademunt, Keshav Unnikrishnan, Yasir Usmani, and Arvind Roshaan. 2026. Retrieval Enhancements for RAG: Insights from a Deployed Customer Support Chatbot. In Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 5: Industry Track), pages 169–180, Rabat, Morocco. Association for Computational Linguistics.