This study demonstrates how large language models (LLMs) such as GPT-4 can facilitate the design of potent molecules. We used this approach to design organic structure-directing agents (OSDAs) that guide the crystallization of zeolites. A computational workflow was developed, wherein the LLM proposed novel OSDAs to stabilize targeted zeolites. The suggested candidates underwent evaluation through empirical screening criteria and atomistic simulation. Feedback was then provided to the LLM in natural language to refine subsequent proposals. The predicted candidates encompassed experimentally validated OSDAs, structurally analogous ones, and novel ones with superior affinity scores, underscoring the robust capability of the LLM.