  Comprehensive planning agents have been a long term goal in the field of
artificial intelligence. Recent innovations in Natural Language Processing have
yielded success through the advent of Large Language Models (LLMs). We seek to
improve the travel-planning capability of such LLMs by extending upon the work
of the previous paper TravelPlanner. Our objective is to explore a new method
of using LLMs to improve the travel planning experience. We focus specifically
on the "sole-planning" mode of travel planning; that is, the agent is given
necessary reference information, and its goal is to create a comprehensive plan
from the reference information. While this does not simulate the real-world we
feel that an optimization of the sole-planning capability of a travel planning
agent will still be able to enhance the overall user experience. We propose a
semi-automated prompt generation framework which combines the LLM-automated
prompt and "human-in-the-loop" to iteratively refine the prompt to improve the
LLM performance. Our result shows that LLM automated prompt has its limitations
and "human-in-the-loop" greatly improves the performance by $139\%$ with one
single iteration.
