面向客户端开发的生产级AI编码系统
Production-Grade AI Coding System for Client-Side Development
AI总结：
本文提出了一种面向客户端开发的生产级AI编码系统，通过结构化多阶段流程整合Figma设计、PRD和领域知识，提升代码生成与产品需求的对齐性。
摘要：
在现实世界中将基于大语言模型的代码生成应用于客户端开发仍然具有挑战性，这主要是由于输入的异质性、严格的工程约束以及产品需求文档（PRD）中复杂的交互逻辑。现有的设计到代码方法往往专注于视觉翻译或单次生成，难以可靠地将生成的代码与生产要求对齐。本文提出了一种面向客户端开发的生产级AI编码系统，该系统在现实工业约束下设计，采用结构化、多阶段的流程，将Figma设计、自然语言PRD和领域特定的工程知识整合到显式中间产物中，从而实现受控规划和逐步代码生成。通过将PRD理解扎根于具体的UI组件，系统提高了产品需求与实现之间的对齐性。我们评估了该系统在来自生产客户端项目的真实数据集上的表现。结果表明，领域特定的适应显著提高了PRD理解的准确性，而端到端评估显示了高UI保真度和在现实案例中交互逻辑的稳健实现。这些发现表明，结构化、以产物为中心的流程为生产级AI编码系统提供了实用的基础。
英文摘要：
Deploying large language model-based code generation in real-world client-side development remains challenging due to heterogeneous inputs, strict engineering constraints, and complex interaction logic expressed in product requirement documents (PRDs). Existing design-to-code approaches often focus on visual translation or single-shot generation, and struggle to reliably align generated code with production requirements. This paper presents a production-grade AI coding system designed for client-side development under realistic industrial constraints. The system adopts a structured, multi-stage pipeline that integrates Figma designs, natural-language PRDs, and domain-specific engineering knowledge into explicit intermediate artifacts, enabling controlled planning and incremental code generation. By grounding PRD understanding in concrete UI components, the system improves alignment between product requirements and implementation. We evaluate the system on proprietary but realistic datasets derived from production client-side projects. Results show that domain-specific adaptation significantly improves PRD understanding accuracy, while end-to-end evaluations demonstrate high UI fidelity and robust implementation of interaction logic in real-world cases. These findings suggest that structured, artifact-centric pipelines provide a practical foundation for production-grade AI coding systems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
