Process design improvements require extensive knowledge, considerable time, and huge human resources due to the complexity

of chemical processes and their diverse objective functions. However, machine learning-based approaches using

vast accumulated data are limited in low versatility, applicable only to specifi c processes, and unable to understand the basis

of model decisions. This study proposes the GPT-based Improved Process Hybrid Transformer (GIPHT), a process design

improvement generation system utilizing Large Language Model (LLM). LLMs, being natural language-based, allow for

understanding the basis of model decisions without need of explainable AI analysis. GIPHT is composed of multi-agent to

enhance versatility and performance for diverse chemical processes. We also propose the Detailed Simplifi ed Flowsheet Input

Line Entry System format to express process diagrams in natural language, including enhanced information about process

conditions. A structured prompt system is employed and validated in the LLM domain through prompt engineering. GIPHT

searches and extracts data based on its proposed improvement methodology, providing explanations for the decision-making

process and the basis, overcoming limitations of the traditional black-box AI models. It off ers directional ideas to design

engineers in the early stages of process design and would be used for training of process engineers, supporting improvement

of outdated processes and transformation into more environmentally friendly processes.