约束驱动的上下文工程：为AI系统设计领域接口
Constraint-Driven Context Engineering: Designing Domain Interfaces for AI Systems
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中文总结 AI 辅助
本文提出约束驱动的上下文工程（CDCE），一种将领域约束作为设计驱动因素来为AI系统设计领域接口的方法，并通过多案例研究验证其在不同领域的适用性。
中文摘要 AI 辅助
生成式AI系统越来越多地被部署以解决领域问题。这些系统在技术、监管、制度和规范性约束下运行，这些约束定义了其领域内可接受的AI行为和结果。我们在行业合作中观察到一种反复出现的模式：合作伙伴通常带着一个功能正常但相对通用的AI解决方案而来。挑战不再是从头构建AI系统，而是提高AI生成解决方案的质量和领域适用性。在这些情境中，限制因素往往是系统可用上下文的质量、范围和结构。然而，现有的上下文工程方法主要侧重于通过检索、记忆和工具提供领域知识，对系统性地识别和操作化治理AI系统在其运行环境中约束的支持有限。\n本文提出了约束驱动的上下文工程（CDCE），一种为AI系统设计领域接口的设计方法。借鉴软件架构设计和领域驱动设计（DDD），CDCE将领域约束视为一等设计驱动因素。它识别和描述约束，确定所需的上下文资产，并设计通过这些资产可供AI系统使用的表示形式。\n我们与行业和公共部门合作伙伴进行了一项比较性多案例研究，涵盖教育评估、医疗决策支持和财务困境预测。根据其特性，约束可以引导AI行为、强制执行允许的边界，或支持对AI生成结果的验证。这些案例展示了CDCE在不同领域中的适用性，并展示了约束特性如何塑造最终的领域接口。
英文摘要
Generative AI systems are increasingly deployed to address domain problems. These systems operate under technical, regulatory, institutional, and normative constraints that define acceptable AI behaviour and outcomes within their domains. We observe a recurring pattern in our industry engagement: partners often arrive with a functioning but relatively generic AI solution. The challenge is no longer to build an AI system from scratch, but to improve the quality and domain appropriateness of an AI-generated solution. In these settings, the limiting factor is often the quality, scope, and structure of the context available to the system. Yet, existing context engineering approaches primarily focus on supplying domain knowledge through retrieval, memory, and tools, with limited support for systematically identifying and operationalising the constraints that govern AI systems in their operational environments. This paper proposes Constraint-Driven Context Engineering (CDCE), a design approach for engineering domain interfaces for AI systems. Drawing on software architecture design and Domain-Driven Design (DDD), CDCE treats domain constraints as first-class design drivers. It identifies and characterises constraints, determines the required context assets, and designs representations through which these assets are made available to AI systems. We conducted a comparative multiple-case study with industry and public-sector partners across educational assessment, healthcare decision support, and financial-distress prediction. Depending on their characteristics, constraints can guide AI behaviour, enforce permissible boundaries, or support verification of AI-generated outcomes. The cases demonstrate CDCE's applicability across contrasting domains and show how constraint characteristics shape the resulting domain interfaces.
