PromptMN: Pseudo Prompting Language

2026-06-17 · Source: cs.SE updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Advanced, extended

Summary

PromptMN is a pseudo-prompting domain-specific language designed to enhance human-to-generative AI interaction by addressing the fragility of natural language prompts. It annotates prose with compact, %-prefixed typed directives covering roles, goals, requirements, and constraints, allowing semantic resolution regardless of authoring order. Positioned between informal prompting and programming-style pseudocode, PromptMN offers inspectable and reusable prompt structures. These are lightweight enough for analysts, managers, and developers across the software development lifecycle (SDLC). The language also supports reverse prompt engineering, enabling users to inspect a model's inferred intent before execution. Feasibility tests on Claude Fable 5, Claude Opus 4.8, Gemini 3.1 Pro, and GPT-5.5 demonstrated correct resolution of complex instructions, including repetition and conditionals, without fine-tuning. This suggests a practical path to clearer human-AI communication.

Key takeaway

For AI Engineers and Prompt Engineers building agentic workflows or AI-assisted development tools, PromptMN offers a critical solution to prompt fragility. By structuring intent with typed directives like %role, %goal, and %plan, you can make assumptions and constraints explicit. This reduces misinterpretations and costly rework. Consider adopting PromptMN for complex, multi-step AI tasks. It enhances prompt inspectability, reusability, and overall system reliability, especially when aligning diverse stakeholders and AI tools.

Key insights

PromptMN structures natural language prompts with typed directives, enhancing clarity and reliability in human-AI interaction.

Principles

• Explicit prompt structure reduces ambiguity and fragility.
• Semantic resolution enables flexible directive ordering.
• Reverse prompting clarifies model's inferred intent.

Method

Annotate natural language with %-prefixed keywords like %role, %goal, %req, using semicolons to terminate statements and curly braces for block scopes.

In practice

• Use %showplan to preview model's execution plan.
• Employ %trace for post-action reasoning logs.
• Define %method for encapsulating reusable behaviors.

Topics

• PromptMN
• Prompt Engineering
• Domain-Specific Language
• Agentic AI
• Software Development Lifecycle
• Reverse Prompt Engineering

Code references

• denkhzol/PromptMN

Best for: Machine Learning Engineer, NLP Engineer, AI Architect, Prompt Engineer, AI Engineer, Software Engineer

Related on AIssential

• PromptMN: Pseudo Prompting Language — PromptMN is a pseudo-prompting language that adds structured, typed directives to natural language prompts, improving clarity and reusability.
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• We Thought We Were Programming AI — We Were Just Talking to It — The initial view of prompt engineering as deterministic programming is giving way to understanding AI interaction as less predictable conversation.
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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.SE updates on arXiv.org.