  The rapid evolution of Large Language Models (LLMs) has markedly expanded
their application across diverse domains, transforming how complex problems are
approached and solved. Initially conceived to predict subsequent words in
texts, these models have transcended their original design to comprehend and
respond to the underlying contexts of queries. Today, LLMs routinely perform
tasks that once seemed formidable, such as writing essays, poems, stories, and
even developing software code. As their capabilities continue to grow, so too
do the expectations of their performance in even more sophisticated domains.
  Despite these advancements, LLMs still encounter significant challenges,
particularly in scenarios requiring intricate decision-making, such as planning
trips or choosing among multiple viable options. These tasks often demand a
nuanced understanding of various outcomes and the ability to predict the
consequences of different choices, which are currently outside the typical
operational scope of LLMs.
  This paper proposes an innovative approach to bridge this capability gap. By
enabling LLMs to request multiple potential options and their respective
parameters from users, our system introduces a dynamic framework that
integrates an optimization function within the decision-making process. This
function is designed to analyze the provided options, simulate potential
outcomes, and determine the most advantageous solution based on a set of
predefined criteria. By harnessing this methodology, LLMs can offer tailored,
optimal solutions to complex, multi-variable problems, significantly enhancing
their utility and effectiveness in real-world applications. This approach not
only expands the functional envelope of LLMs but also paves the way for more
autonomous and intelligent systems capable of supporting sophisticated
decision-making tasks.
