Prompt Engineering Standards (RFC)

"The world of AI prompting is currently a jungle. There are many beast trails, but no paved roads. I got tired of the chaos, so I built a highway."

🛑 Stop Guessing. Start Engineering.

You are likely here because you are tired of:

• "Magic spells" that work today but break tomorrow.
• Prompts that look like a stream of consciousness.
• Debugging AI hallucinations with no clear methodology.

This repository is not a collection of "tips and tricks." It is a Syntax Standard (RFC) for Prompt Engineering.

Just as we have HTTP for the web and PEP8 for Python, we need a standard for communicating with LLMs.

📚 The Standard

Read the Standards (v0.3.0) — Pre-release

Core Concepts

• Markdown-Native: Use the format LLMs were trained on.
• The "Markdown + XML" Hybrid: Use Markdown for instructions, XML for data boundaries.
• Explicit Variable Declaration: {variable}: """value""".

⭐ New in v0.3.0

• CO-STAR Framework: Enterprise-grade 6-element prompt structuring (Context, Objective, Style, Tone, Audience, Response).
• Structured Reasoning: SCoT (Structured Chain of Thought), CoVe (Chain of Verification), CoD (Chain of Density).
• LLMOps: Version control, observability, evaluation testing, and governance frameworks for prompts.

🤝 Contributing

This standard is open source (CC-BY-SA 4.0). It is designed to be evolved by the community.

See CONTRIBUTING.md to propose changes (PEPs).

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A Note from the Architect

I am a nameless senior architect who has spent years building complex systems. Currently, I coach engineers on AI implementation while developing an autonomous agent where 11 nodes organically collaborate for code reviews.

I realized that 90% of "prompt engineering" problems are actually just syntax errors. We are trying to write code in natural language without a compiler.

This standard is my attempt to provide that compiler. It is the "Paved Road" through the jungle.

Walk with me.

— The Architect