ChatGPT faces significant hurdles in applying clean architecture principles to legacy systems due to its inability to directly comprehend the specific codebase and its historical context. It struggles with the inherent architectural drift and technical debt commonly found, often proposing ideal solutions that might be impractical or require immense refactoring efforts. The quality of recommendations is heavily dependent on the user's ability to provide accurate and comprehensive descriptions of the complex, often undocumented, legacy behaviors and business rules. Furthermore, tackling tightly coupled components in legacy environments makes incremental adoption of clean architecture patterns exceedingly difficult without introducing new risks or breaking existing functionality. ChatGPT also cannot perform critical risk assessment or cost-benefit analysis for proposed changes, leaving a crucial gap in practical implementation guidance. Its general knowledge, while vast, might also fall short when dealing with highly specific or outdated legacy technologies and frameworks, requiring extensive human oversight and adaptation. More details: https://www.gmwebsite.com/web/redirect.asp?url=https://abcname.com.ua/