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. This approach not only enhances the reliability and practicality of LLM-based database querying but also empowers users to have more control over the querying process. By providing a more transparent and controllable experience, QueryGenie has the potential to revolutionize the way we interact with databases and unlock new possibilities for data-driven applications.