The Reality of Prompt Engineering: Simplicity Often Outperforms Sophistication in Reasoning Tasks

Abstract Despite the proliferation of prompt engineering techniques for large language models, research outputs present contradictory findings about optimal strategies, with some studies showing substantial improvements while others demonstrate minimal effects. We conducted a two-phase comprehensive evaluation to systematically assess prompt effectiveness across challenging reasoning tasks. Our primary investigation tested five prominent prompt engineering techniques (zero-shot, few-shot, chain-of-thought, role-playing, and deliberation) across 27 reasoning tasks from BIG-Bench Hard using GPT-4o-mini—chosen as a cost-efficient and accessible model for practical deployment scenarios. We selected reasoning tasks because they enable direct comparison to human cognitive performance and provide objective evaluation metrics. The initial phase tested 13,500 queries (2,700 questions across 5 prompt types). Building on these findings, our second phase introduced task-specific variants—specialized role-play prompts and task-specific few-shot examples—testing an additional 5,400 queries across the same tasks. Overall performance rankings revealed that task-specific role-play achieved the highest accuracy (87.78%), fol-lowed closely by chain-of-thought (87.63%) and role-playing (87.15%). Surprisingly, zero-shot prompting (84.85%) significantly outperformed few-shot prompting (80.70%) by 4.15 percentage points. A critical discovery emerged from response analysis: all prompt types spontaneously exhibit chain-of-thought-style reasoning patterns, even when not explicitly instructed, suggesting that step-by-step reasoning represents an internalized model behavior rather than a prompt-dependent phenomenon. Statistical analysis across both phases (18,900 total queries) revealed 14 significant performance differences (p < 0.05), with few-shot approaches consistently under-performing. Notably, role-play and zero-shot prompting proved most efficient in token usage and response time while maintaining competitive accuracy. The results demonstrate that prompt complexity does not guarantee superior performance, and that task-domain alignment matters more than assuming sophisticated prompts yield better results. Our findings provide evidence-based guidance challenging conventional assumptions about prompt engineering effectiveness.