  Prompt design plays a crucial role in shaping the efficacy of ChatGPT,
influencing the model's ability to extract contextually accurate responses.
Thus, optimal prompt construction is essential for maximizing the utility and
performance of ChatGPT. However, sub-optimal prompt design may necessitate
iterative refinement, as imprecise or ambiguous instructions can lead to
undesired responses from ChatGPT. Existing studies explore several prompt
patterns and strategies to improve the relevance of responses generated by
ChatGPT. However, the exploration of constraints that necessitate the
submission of multiple prompts is still an unmet attempt. In this study, our
contributions are twofold. First, we attempt to uncover gaps in prompt design
that demand multiple iterations. In particular, we manually analyze 686 prompts
that were submitted to resolve issues related to Java and Python programming
languages and identify eleven prompt design gaps (e.g., missing
specifications). Such gap exploration can enhance the efficacy of single
prompts in ChatGPT. Second, we attempt to reproduce the ChatGPT response by
consolidating multiple prompts into a single one. We can completely consolidate
prompts with four gaps (e.g., missing context) and partially consolidate
prompts with three gaps (e.g., additional functionality). Such an effort
provides concrete evidence to users to design more optimal prompts mitigating
these gaps. Our study findings and evidence can - (a) save users time, (b)
reduce costs, and (c) increase user satisfaction.
