  Recognizing the imperative to address the reliability and transparency issues
of Large Language Models (LLM), this work proposes an LLM maturity model
tailored for text-to-query applications. This maturity model seeks to fill the
existing void in evaluating LLMs in such applications by incorporating
dimensions beyond mere correctness or accuracy. Moreover, this work introduces
a real-world use case from the law enforcement domain and showcases QueryIQ, an
LLM-powered, domain-specific text-to-query assistant to expedite user workflows
and reveal hidden relationship in data.
