Production-Grade AI Coding System for Client-Side Development

TL;DR

This paper introduces a structured, multi-stage AI coding system tailored for client-side development, effectively integrating design, requirements, and engineering knowledge to produce reliable, production-ready code.

Contribution

The paper presents a novel multi-stage pipeline that combines design, natural language requirements, and engineering knowledge to improve AI code generation for real-world client-side projects.

Findings

Enhanced PRD understanding accuracy with domain-specific adaptation

High UI fidelity achieved in generated code

Robust implementation of interaction logic in real-world scenarios

Abstract

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.