The rapid integration of generative artificial intelligence (GenAI) into academic library discovery systems offers new opportunities but also introduces challenges, as vendor tools streamline natural language searching while obscuring search logic. This article presents a case study of a librarian-designed AI discovery tool that is focused on transparency and pedagogy. The assistant converts natural language queries into structured, editable Boolean search strings; surfaces search logic; and retrieves results across library holdings. Developed through iterative prompt engineering, this tool demonstrates how GenAI can support research practices without sacrificing rigor. Findings from qualitative feedback and quantitative assessment indicate that the tool improves the consistency and quality of Boolean query construction while fostering user engagement with search as a reflective process. This case study argues that libraries can play a critical role in shaping AI discovery tools that align with information literacy goals and institutional scholarly standards. By foregrounding transparency and user agency, librarian-led AI design positions GenAI as a form of instructional scaffolding rather than a replacement for expertise.
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