@inproceedings{li-etal-2026-example,
    title = "Example Quality Matters: Multi-Aspects Example Augmentation for Private Library Programming",
    author = "Li, Yuhao  and
      Sun, Haifeng  and
      Zhang, Xuesong  and
      Yao, Shu  and
      Zheng, Haoyu  and
      Wang, Yvchuan  and
      Wang, Huazheng  and
      Zhuang, Zirui  and
      Qi, Qi  and
      Liao, Jianxin  and
      Wang, Jingyu",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.1731/",
    pages = "37311--37327",
    ISBN = "979-8-89176-390-6",
    abstract = "Recent advances in large language models (LLMs) have significantly improved code-generation capabilities, particularly through retrieval-augmented generation (RAG) for private libraries. While RAG leverages API documentation to address the scarcity of private code corpora, its performance critically depends on the quality of retrieved examples. Existing approaches often overlook the intrinsic characteristics of these examples, particularly how factors such as complexity, readability, and correctness impact their effectiveness. In this study, we systematically investigate these three critical aspects{---}complexity, readability, and correctness{---}and find that optimal examples should exhibit moderate complexity, semantic correctness, and step-by-step execution patterns. Based on these findings, we propose ComboPrompt, a novel example enhancement method that strategically combines existing API examples to improve complexity, refines code structure for readability, and incorporates automated validation ensuring correctness. Extensive evaluations across five private library benchmarks and different LLMs demonstrate that ComboPrompt achieves up to 22{\%} accuracy improvement over baseline approaches. Code is available at [Anonymous Github](https://github.com/FireAndWin/ComboPrompt{\_}ExampleQualityMatters)."
}