The fashion industry faces significant challenges due to overproduction and waste, often driven by uncertainty about consumer preferences. This paper presents ReVisE, a novel framework leveraging generative AI to address this issue by emulating outfit generation from user reviews. ReVisE combines a text-to-text Large Language Model (LLM) and a text-to-image Stable Diffusion (SD) model to create virtual outfits based on customer feedback. The LLM consolidates user reviews to extract desired improvements and feedback, and the SD model utilizes these insights to produce realistic visual representations of the improved product. Our framework allows designers to evaluate potential designs and identify areas for improvement without physically producing multiple prototypes, thereby reducing waste and accelerating the design process. Experimental results conducted on the Amazon fashion item reviews demonstrate the effectiveness of ReVisE, showing promising results with both multimodal and human evaluations.