Using ChatGPT for database queries in legacy systems presents significant challenges, primarily stemming from the inherent complexity and poor documentation of older systems. ChatGPT struggles with understanding obscure database schemas, often lacking clear naming conventions or comprehensive metadata that describe table relationships and business logic. A critical hurdle is data security and privacy, as exposing sensitive information or granting an AI direct access to proprietary legacy databases raises compliance risks and concerns about data leakage. Furthermore, legacy systems frequently employ unique SQL dialects and complex stored procedures that generic LLMs may misinterpret or fail to utilize correctly, leading to inefficient or incorrect queries. This necessitates extensive manual validation and testing to ensure accuracy and prevent potential performance degradation on fragile, older infrastructure. More details: https://marshmallow.halfmoon.jp/weblog/m/index.cgi?id=1&cat=5&mode=redirect&no=27&ref_eid=81&url=https://abcname.com.ua/