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arXiv · 2508.15146

QueryGenie: Making LLM-Based Database Querying Transparent and Controllable

Longfei Chen·Shenghan Gao·Shiwei Wang·Ken Lin·Yun Wang·Quan Li
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Abstract

Conversational user interfaces powered by large language models (LLMs) have significantly lowered the technical barriers to database querying. However, existing tools still encounter several challenges, such as misinterpretation of user intent, generation of hallucinated content, and the absence of effective mechanisms for human feedback-all of which undermine their reliability and practical utility. To address these issues and promote a more transparent and controllable querying experience, we proposed QueryGenie, an interactive system that enables users to monitor, understand, and guide the LLM-driven query generation process. Through incremental reasoning, real-time validation, and responsive interaction mechanisms, users can iteratively refine query logic and ensure alignment with their intent.

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Longfei Chen, Shenghan Gao, Shiwei Wang, Ken Lin, Yun Wang, Quan Li. 2025-08-21. QueryGenie: Making LLM-Based Database Querying Transparent and Controllable. https://arxiv.org/abs/2508.15146

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