让AI生成代码时自动对齐架构约束，提升生产可用性。
Architectural Constraints Alignment in AI-assisted, Platform-based Service Development
- 用检索增强+智能追问，动态发现并解决架构模糊点。
- 相比通用AI生成，架构一致性提升，部署成功率更高。
- 适合需要稳定上线的工程团队使用。
AI辅助开发工具虽能快速原型化服务，但常忽略生产环境中的架构约束、基础设施依赖和组织规范，导致生成代码脆弱且难以部署。本文提出一种检索增强的代码骨架构建方法，结合平台化代码生成与代理式澄清循环，主动暴露并解决架构约束的歧义。通过模板检索与结构化交互结合，在服务搭建阶段嵌入生产相关考量。评估表明，该方法在架构一致性和可部署性上优于通用AI代码生成流程，说明约束感知的检索对对齐AI辅助开发与生产实践至关重要。
原文摘要 · Abstract (English)
AI-assisted development tools enable rapid prototyping of services but often lack awareness of architectural constraints, infrastructure dependencies, and organizational standards required in production environments. Consequently, generated artifacts may exhibit brittle behavior and limited deployability. We propose a retrieval-augmented scaffolding approach that combines platform-based code generation with agentic clarification loops to expose and resolve architectural constraint ambiguities. By combining template retrieval with structured interaction, the method embeds production-relevant considerations during service scaffolding. Evaluation indicates improved architectural consistency and deployability compared to general-purpose AI code generation workflows, suggesting that constraint-aware retrieval is essential for aligning AI-assisted service development with production software engineering practices.
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