Innovative Architecting of Thought: A Case Study on Cross-Model Validation of Declarative Prompts—Introducing a Novel Zero-Shot Prompting Technique with Verifiable Prompts for Frontier Models

Innovative Framework for Cross-Model Validation of Declarative Prompts: A Case Study in Verifiable AI Prompting

Introduction: Rethinking Human-AI Interaction Through Declarative Prompts

The evolving landscape of artificial intelligence (AI) interaction is revealing a paradigm shift—from informal, conversational exchanges to precisely engineered, machine-readable instructions known as Declarative Prompts (DPs). Unlike traditional prompts, DPs serve as explicit cognitive contracts—formalized blueprints that articulate the AI’s objectives, preconditions, constraints, invariants, and validation criteria. This approach elevates prompt design from ad hoc querying to a disciplined architectural practice, ensuring clarity, robustness, and consistency in AI outputs.

This shift underscores a foundational principle: effective human-AI collaboration hinges on well-structured, verifiable instructions that guide models reliably, minimizing semantic drift and enhancing interpretability.

Methodology: Designing and Validating a Cross-Model Prompting Framework

To evaluate the robustness and generalizability of Declarative Prompts, a systematic cross-model validation methodology was developed within the Context-to-Execution Pipeline (CxEP) framework. This process involves selecting a highly structured Declarative Prompt tailored as a Product-Requirements Prompt (PRP), embedding complex reasoning scaffolds such as role-based directives and explicit Chain-of-Thought (CoT) instructions.

The validation deployed this DP across a diverse set of state-of-the-art large language models (LLMs)—including Gemini, Copilot, DeepSeek, Claude, and Grok—aiming to demonstrate that the prompt’s effectiveness derives from its architectural integrity rather than model-specific nuances.

Key Components of the Validation Protocol Include:

• Persistent Context Anchoring (PCA): Embedding all necessary knowledge directly within the prompt to eliminate external knowledge dependencies that might vary across models.

• Structured Context Injection: Explicitly tagging instructions and sources to guide reasoning pathways.

• Automated Self-Testing: Incorporating machine-readable validation criteria—such as adherence to format and logical coherence—to enable objective assessment of output quality.

• Traceability & Auditability: Maintaining comprehensive logs of prompts and responses for provenance verification.

This rigorous approach ensures that the prompt’s efficacy is rooted in its architecture and explicit instructions rather than superficial model tricks.

Results: Analyzing Emergent Capabilities and Model Behavior

Applying the structured Declarative Prompt across multiple models yielded insights into their respective “personas” and reasoning patterns, effectively constituting an “AI orchestra” that demonstrated