Architecting Thought: A Case Study in Cross-Model Validation of Declarative Prompts! I Created/Discovered a completely new prompting method that worked zero shot on all frontier Models. Verifiable Prompts included

Architecting Robust AI Interactions: A Case Study in Cross-Model Validation of Declarative Prompts

Introduction: Elevating Prompt Engineering to Architectural Discipline

In the rapidly evolving landscape of human-AI collaboration, the paradigm is shifting from loosely structured conversational queries toward meticulous, purpose-driven prompt design. This shift introduces the concept of Declarative Prompts (DPs)—structured, machine-readable blueprints that serve as contractual specifications for AI behavior. Unlike traditional prompts that merely pose questions, DPs encode explicit goals, preconditions, constraints, invariants, and self-validation criteria directly within their architecture. This transformation elevates prompt engineering from an art of clever phrasing to a rigorous architectural discipline, fostering greater clarity, consistency, and verifiability in AI outputs.

The core thesis posits that well-crafted DPs act as cognitive contracts—formal specifications that guide AI models towards desired behaviors while minimizing semantic drift. By embedding explicit directives and verifiable markers, DPs establish a non-negotiable foundation for human-AI interaction, ensuring that each response aligns with intended objectives.

Methodology: Designing a Cross-Model Validation Framework

To validate the efficacy and robustness of Declarative Prompts, a systematic cross-model validation experiment was devised within the Context-to-Execution Pipeline (CxEP) framework. This approach centers on testing a singular, highly structured DP—specifically, a Product-Requirements Prompt (PRP)—across a diverse set of advanced Large Language Models (LLMs), including Gemini, Copilot, DeepSeek, Claude, and Grok.

Key components of the methodology include:

• Selection of the Declarative Prompt: The chosen DP is designed with comprehensive cognitive scaffolding—integrating role-based prompting, explicit chain-of-thought instructions, and detailed goal-setting—to elicit structured reasoning consistent across models.

• Cross-Model Validation Strategy: Applying the DP to multiple LLMs ensures that observed behaviors are attributable to the prompt’s architectural integrity rather than model-specific quirks. This confirms the prompt’s generalizability and robustness.

• Execution Protocol (CxEP Integration): The experiment employs several mechanisms:

• Persistent Context Anchoring (PCA): All relevant knowledge is embedded directly within the prompt, avoiding reliance on external sources that may lack domain-specific information.
• Structured Context Injection: Clear demarcation of instructions and knowledge sources using tags to prioritize context-based reasoning.
• Automated Self-Test and Validation: Including machine