Strengthening Human-Centric Chain-of-Thought Reasoning Integrity in LLMs via a Structured Prompt Framework

Prompt engineering offers a practical alternative to expensive model improvements: Rather than relying on costly approaches like model scaling or fine-tuning, the study demonstrates that carefully structured prompts can improve LLM reasoning by up to 40% while remaining transparent, auditable, and computationally lightweight—making it accessible for organizations with limited resources.

Structured prompts reduce hallucination and reasoning errors in security-critical tasks: The framework's 16 factors across four dimensions (Context Control, Evidence Grounding, Reasoning Structure, and Security Constraints) explicitly address common LLM failures like hallucination and "reasoning drift," which is particularly important when AI systems are used to detect threats or analyze sensitive information.

Interpretability matters for responsible AI deployment: By strengthening how LLMs explain their reasoning steps, the framework makes AI decision-making more auditable and understandable to human experts—a key requirement for responsible AI in security-sensitive applications where stakeholders need to verify and trust the system's conclusions.

Human-centered evaluation reveals gaps that standard metrics miss: The study emphasizes structured human evaluation rather than relying solely on automated metrics, suggesting that responsible AI development requires direct assessment by domain experts to catch reasoning failures that automated benchmarks might overlook.

Transparency and controllability are achievable without sacrificing performance: The research demonstrates that simpler, more interpretable approaches to improving LLM reasoning don't require trading off accuracy, challenging the assumption that "black box" scaling is necessary for better AI performance.