  Large Language Models with transformer architecture have revolutionized the
domain of text generation, setting unprecedented benchmarks. Despite their
impressive capabilities, LLMs have been criticized for generating outcomes that
deviate from factual accuracy or display logical inconsistencies, phenomena
commonly referred to as hallucinations. This term, however, has often been
misapplied to any results deviating from the instructor's expectations, which
this paper defines as attention misdirection rather than true hallucinations.
Understanding the distinction between hallucinations and attention misdirection
becomes increasingly relevant in business contexts, where the ramifications of
such errors can significantly impact the value extraction from these inherently
pre-trained models. This paper highlights the best practices of the PGI,
Persona, Grouping, and Intelligence, method, a strategic framework that
achieved a remarkable error rate of only 3,15 percent across 4,000 responses
generated by GPT in response to a real business challenge. It emphasizes that
by equipping experimentation with knowledge, businesses can unlock
opportunities for innovation through the use of these natively pre-trained
models. This reinforces the notion that strategic application grounded in a
skilled team can maximize the benefits of emergent technologies such as the
LLMs.
