Designing Cognitive Frameworks: A Case Study on Cross-Model Validation of Declarative Prompts—Introducing a Novel Zero-Shot Prompting Technique with Verifiable Prompts Demonstrated Across Leading Models

Architecting Thought: A Deep Dive into Cross-Model Validation and Verifiable Prompting Strategies

Introduction: Elevating Human-AI Interaction Through Declarative Prompts

In the rapidly evolving domain of artificial intelligence, especially with large language models (LLMs), the quality and reliability of prompts are paramount. Traditionally, prompt engineering has relied heavily on crafting natural language queries. However, this approach can suffer from semantic drift, ambiguity, and inconsistent results when scaling across various models.

This article explores a paradigm shift: the transformation of prompts into Declarative Prompts (DPs)—explicit, structured, machine-readable contracts that serve as blueprints for AI reasoning. Unlike conventional prompts, DPs encode objectives, preconditions, constraints, invariants, and verification criteria directly within the prompt artifact itself. This approach positions prompt engineering as an architectural discipline, emphasizing precision, verifiability, and robustness in human-AI interactions.

Methodology: Systematic Cross-Model Validation Using a Structured Pipeline

To assess the effectiveness of declarative prompts, a comprehensive validation methodology was devised, grounded in the Context-to-Execution Pipeline (CxEP) framework. The core idea: design a highly structured, formal Product-Requirements Prompt (PRP) that embodies complex reasoning scaffolding, including role-based directives and explicit chain-of-thought instructions.

Experimental Design:

• Prompt Selection: The chosen DP formalizes the task, embedding constraints, background knowledge, and self-test criteria. For instance, it specifies roles and explicit formatting guides to ensure consistency across outputs.
• Model Diversity: The prompt is applied across a variety of cutting-edge LLMs—such as Gemini, Copilot, DeepSeek, Claude, and Grok—to ascertain that the prompt’s robustness transcends model-specific capabilities.

• Execution Protocol:

• Persistent Context Anchoring (PCA): Embedding necessary knowledge directly into prompts to mitigate reliance on external data sources, especially relevant for novel frameworks (“Biolux-SDL”).

• Structured Context Injection: Clear demarcation of instructions and knowledge sources via tags, ensuring the model’s reasoning is grounded.
• Automated Self-Testing: Incorporation of machine-readable validation checks within prompts to automatically evaluate output coherence, format adherence, and logical consistency.
• Traceability: Detailed logs capture prompt inputs, model responses, and reasoning traces to enable rigorous auditability.

Results: Emergent Capabilities and Model Behaviors

Applying the