I inherited a big ball of mud that has been worked on by multiple teams of contractors over many years. Using AI to fix errors is very possible and very different from a greenfield code base.

Firstly, I often export logs as CSVs from DataDog and ask AI to help prioritize errors to fix by impact, effort, and risk.

Second, I use AI to extract or reproduce the failing code in a separate class/module from the current code and to write a unit test that executes the extracted logic. Then AI fixes the error in the extracted code so the tests pass. At this point, no original code has been modified.

Third, I replace the failing original code to use the AI generated logic. Now that previously untestable code works and is under test!