Creating beautiful and functional user interfaces (UIs) is a complex process, demanding careful attention to design principles, accessibility, and responsiveness. While Large Language Models (LLMs) like those powering tools such as Vercel's V0 have revolutionized frontend development by generating UI code from simple prompts, the generated code often falls short of professional design standards. A new research project called DesignRepair aims to bridge this gap by automatically detecting and repairing design flaws in LLM-generated frontend code. Imagine effortlessly transforming a rough UI sketch generated by AI into a polished, professional design, all with the help of an intelligent tool. This is the promise of DesignRepair, a dual-stream system that analyzes both the frontend code and the rendered webpage to identify and fix design inconsistencies. It leverages the principles of Google’s Material Design 3, a comprehensive design system, as its knowledge base. This knowledge base is divided into two parts: a component-level knowledge base for fine-grained details like button sizes and text contrast, and a system-level knowledge base for broader design aspects such as layout, color harmony, and typography. DesignRepair works by first extracting key components and properties from the frontend code and rendered page using LLMs and the Playwright testing tool. It then compares these extracted elements against its Material Design knowledge base to identify violations. The system then uses a divide-and-conquer approach, addressing each violation individually before synthesizing the individual fixes into a final, repaired code output. The system distinguishes between “hard” constraints, mandatory guidelines for usability and accessibility, and “soft” constraints, recommendations for enhancing aesthetics and engagement. This nuanced approach allows DesignRepair to prioritize critical fixes while also suggesting improvements for overall design quality. Extensive testing of DesignRepair showed promising results. It successfully identified and repaired a significant percentage of design violations in both AI-generated code and real-world projects from GitHub. A user study further confirmed the effectiveness of the repairs, with participants reporting increased satisfaction with the visual attractiveness and usability of the repaired UIs. While DesignRepair shows immense potential, the researchers acknowledge challenges such as handling complex design principles and the subjective nature of some design choices. Future work will focus on incorporating real-time feedback and refining the system's ability to adapt to evolving design guidelines. This research paves the way for a future where AI can not only generate UI code but also ensure its adherence to design best practices, ultimately simplifying the UI development process and empowering developers to create high-quality, user-friendly interfaces with ease.