I know a similar non-coding founder who was using LLMs to create a full fledged TypeScript based SaaS product and regularly comes to me with high level architecture questions, but also doesn't know or care to figure out what HTTP methods are, variable names are a mishmash of whatever case the LLM decided to generate that day, and there are no tests whatsoever. It's held together by sheer force of manual QA.
This my technique- test the output, make sure it works (in the sense of outputs) the way I want. Test the input edge cases, move on. Occasionally when I can't get it to do what I want the llm suggests things like logging of output between functions etc- in which case they get added, but at the end I ask it to take out all the logging and make the code more concise.
And sometimes it breaks in ways I can't fix - so rolling back or picking a new patch from a know break point becomes important.
16 hours for my first azure pipeline, auto-updates from code to prod, static app including setting up git, vscode, node, azure creds etc. I chose a stack I have never seen at work (mostly see AWS) and I am not a coder. Last code was Pascal in the 1980s.
Which is awesome, and if you wanted to understand the code, it would do an amazing job of tutoring you. I love seeing people being able to solve their own problems without the need for a professional programmer.
The downside I've noticed is if I do this, I can't explain how I "solved the problem" during job interviews. I tried once, "I didn't, chatgpt solved it for me," and they laughed and I didn't get the job, so I stopped admitting that and said I just use chatgpt to quickly write up boilerplate for me.
...but. It does work.