Despite their strong performance, large language models remain highly sensitive to prompt formulation. Prior work has addressed this issue through refined data construction or more complex robustness-oriented training methods. In this work, we reproduce and compare these strategies under controlled conditions, evaluating their effects on worst-case prompt sensitivity and average performance. We find that conventional instruction fine-tuning offers limited reductions in prompt sensitivity: across methods, the average gap between the best- and worst-performing prompts remains 40–57% of performance. Moreover, recent robustness-enhancing methods often fail to outperform simpler data construction strategies; in worst-case settings, at least one simple method performs better in 5 of 8 evaluations. Overall, our findings show that prompt sensitivity remains an open problem, and motivate practitioners to prioritize simpler data construction techniques before adopting more sophisticated robustness methods.