Because using AI takes some practice:

- you should document your best practices in a file and point it to the LLM (the standards are @claude or @agent markdown files

- you should manage context (the larger it gets the weaker the output)

- you should use good and clear prompts

- you should generally make it generate a plan with the requirements (business logic changes focused) and then follow and review the implementation plan (I generally produce both in two different markdown files).

- only then you let it code

The last phase, isn't even the most important to be honest, you can do it manually. But I have found that forcing myself through the first two and having AI find information in the codebase, edge cases in the business logic, propose different solutions, evaluate the impact of the changes is a huge productivity multiplier.

Very often I'm not worn out by the coding part, again, I can do it on my own, it's the finding information and connecting the dots the hard one. In that, it excels and I would struggle (mentally) to go back to jumping from file to file while keeping track of my findings in notes to figure out the wheres, whats and whys.