<![CDATA[Prompt engineering has emerged as a transformative strategy for optimizing Large Language Models (LLMs), offering a cost-effective alternative to full model fine-tuning. In a recent bibliometric review, Fatawi et al. (2024) analyzed 437 Scopus-indexed publications from January 2022 to February 2024, using VOSviewer to identify key thematic clusters—including transformer architectures, deep learning innovations, and few-shot learning—and documenting a fivefold increase in related publications over the review period. Building on their macro-level mapping, this commentary extends the discussion by articulating the strategic and democratizing potential of prompt engineering while addressing critical gaps in methodology and ethical oversight. We critique the review’s reliance on a single English-language database, its exclusion of preprints and non-English sources, and its omission of qualitative insights into user practices and system impacts. In response, we offer concrete recommendations to guide future research: diversify data sources for bibliometric analysis, implement rigorous prompt audit frameworks, conduct longitudinal A/B testing in real-world environments, and adopt mixed-methods approaches to capture human-centered dynamics. We also explore emerging synergies—such as quantum-enhanced NLP and neuro-linguistic prompt design—as promising frontiers for advancing prompt optimization. By addressing these gaps, this commentary aims to ensure that prompt engineering evolves not only as a technical solution but as a responsible and inclusive foundation for next-generation AI development.]]>
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