Prompting has become the primary interface between humans and generative AI, yet many natural language prompts remain fragile: roles, goals, constraints, and expected outputs are often buried in prose or left implicit. In agentic and software development workflows, a misread at the first handoff can propagate through every step, since a significant portion of agent failures stem from context ambiguities rather than model limitations. This paper introduces PromptMN, a pseudo-prompting domain-specific language that annotates natural language with compact, %-prefixed typed directives covering roles, goals, requirements, priorities, constraints, plans, inputs, and outputs. Semantic resolution lets authors write in any order while the model interprets directives by function. PromptMN sits between informal prompting and programming-style pseudocode: structured enough to be inspectable and reusable, yet lightweight enough for analysts, managers, developers, and stakeholders across the software development lifecycle (SDLC). PromptMN also pairs with reverse prompt engineering. Asking a model to restate a desired outcome as PromptMN lets users inspect the inferred roles, goals, constraints, and missing assumptions before acting, reducing repair cycles and yielding a reusable artifact for aligning people and AI tools. PromptMN's feasibility is evaluated across several frontier models, including Claude Fable 5, Claude Opus 4.8, Gemini 3.1 Pro, and GPT-5.5. The models correctly resolved PromptMN instructions, including complex structures such as repetition, conditionals, methods, and a prime-checking task, without fine-tuning. The same vocabulary applies across new codebases, maintenance, and redesign in the SDLC scenarios presented. While large-scale validation remains future work, these early results suggest PromptMN is a practical step toward clearer, more reviewable human-to-AI interaction.

翻译：提示已成为人类与生成式AI之间的主要交互界面，然而许多自然语言提示依然脆弱：角色、目标、约束条件和预期输出常常被埋没在散文式描述中或隐含存在。在智能体工作流和软件开发流程中，首次交接时的误读会沿着每个环节传播放大——相当一部分智能体失败源于上下文歧义，而非模型能力局限。本文提出PromptMN，一种伪提示领域专用语言，通过以百分号%为前缀的紧凑类型化指令标注自然语言，覆盖角色、目标、需求、优先级、约束条件、计划、输入和输出。语义解析机制允许作者以任意顺序撰写内容，而模型按功能解读指令。PromptMN兼具非正式提示与编程式伪代码的特性：结构足够规范以支持检查与复用，同时轻量灵活，适用于软件开发生命周期中的分析师、管理者、开发者及利益相关方。PromptMN还与逆向提示工程配合使用——要求模型将期望结果重述为PromptMN格式，可使用户在采取行动前检查推断出的角色、目标、约束条件及缺失假设，从而减少修复循环，并产出可复用的人工智能协作对齐产物。我们跨多个前沿模型评估PromptMN的可行性，包括Claude Fable 5、Claude Opus 4.8、Gemini 3.1 Pro和GPT-5.5。在无需微调条件下，这些模型正确解析了PromptMN指令，涵盖重复、条件分支、方法及素数判断等复杂结构。针对所呈现的软件开发生命周期场景（新代码库、维护、重新设计），相同的语汇体系均适用。虽然大规模验证留待未来工作，但初步结果表明PromptMN是迈向更清晰、更可审查的人机交互的实用一步。