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