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This paper presents a novel approach named Persona-Grouping-Intelligence(PGI), which has been crafted to tackle the challenges posed by GPT models whenapplied to real-world business issues. PGI leverages the inherent capabilitiesof the GPT model to comprehend intricate language structures and generateresponses that are contextually relevant. The experiment occurred in a businessscenario where human intelligence was being underutilized due to less optimizedbusiness processes. The primary objective of this approach is to leverage GPTmodels to reduce the workload on humans in tasks that are extensive,monotonous, and repetitive. Instead, the focus is redirected towarddecision-making activities. Remarkably, the experiment yielded an accuracy rateof 93.81% in validating 4,000 responses generated by the model, underscoringthe effectiveness of the PGI strategies. Effectively addressing the issue ofunderutilized human intelligence, this paradigm shift aligns businessenvironments with dynamic machine intelligence, enabling them to navigate theintricacies of real-world challenges. This approach facilitates the practicalutilization of these models to tackle actual problems. The methodology offersan opportunity to reshape the fundamental structure of business processes byseamlessly integrating human decision-making with adaptable machineintelligence. Consequently, this optimization enhances operational efficiencyand elevates strategic decision-making across diverse business contexts.

Transforming the Output of Generative Pre-trained Transformer: The Influence of the PGI Framework on Attention Dynamics