How about sharing your working prompts then, so that others can learn from it?
- tell it to ask you clarifying questions, repeatedly. it will uncover holes and faulty assumptions and focus the implementation once it gets going
- small features, plan them, implement them in stages, commit, PR, review, new session
- have conventions in place, coding style, best practices, what you want to see and don't want to see in a codebase. we have conventions for python code, for frontend code, for data engineering etc.
- make subagents work for you, to look at a problem from a different angle (and/or from within a different LLM altogether)
- be always critical and dig deeper if you have the feeling that something is off or doesn't make sense
- good documentation helps the machine as well as the human
And the list goes on.