Transforming the Output of Generative Pre-trained Transformer: The
  Influence of the PGI Framework on Attention Dynamics
Aline Ioste
This paper presents a novel approach named Persona-Grouping-Intelligence
(PGI), which has been crafted to tackle the challenges posed by GPT models when
applied to real-world business issues. PGI leverages the inherent capabilities
of the GPT model to comprehend intricate language structures and generate
responses that are contextually relevant. The experiment occurred in a business
scenario where human intelligence was being underutilized due to less optimized
business processes. The primary objective of this approach is to leverage GPT
models to reduce the workload on humans in tasks that are extensive,
monotonous, and repetitive. Instead, the focus is redirected toward
decision-making activities. Remarkably, the experiment yielded an accuracy rate
of 93.81% in validating 4,000 responses generated by the model, underscoring
the effectiveness of the PGI strategies. Effectively addressing the issue of
underutilized human intelligence, this paradigm shift aligns business
environments with dynamic machine intelligence, enabling them to navigate the
intricacies of real-world challenges. This approach facilitates the practical
utilization of these models to tackle actual problems. The methodology offers
an opportunity to reshape the fundamental structure of business processes by
seamlessly integrating human decision-making with adaptable machine
intelligence. Consequently, this optimization enhances operational efficiency
and elevates strategic decision-making across diverse business contexts.