Evaluation reveals structured prompting enhances accuracy and reduces hallucinations in large language models, suggesting effective strategies for AI output quality.
Large Language Models (LLMs) have significantly transformed artificial intelligence by enabling advanced reasoning, natural language understanding, and content generation. While architectural advancements and large-scale datasets contribute to their effectiveness, prompt engineering has emerged as a critical factor influencing output quality and reliabilityThis study presents a structured empirical evaluation of five prompting strategies: unstructured, role-based, chain-of-thought (CoT), instructional, and constraint-based prompting. The findings indicate that structured prompting techniques improve accuracy by up to 35% in reasoning-intensive tasks and significantly reduce hallucinations. Key Contributions: Development of a structured evaluation framework for prompt strategies. Comparative analysis across multiple task domains. Quantitative assessment of reasoning and safety improvements.
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Jabez et al. (2026) studied this question.