Earlier quoted context omitted.
Something else to consider. I often have much better success with something like: Create a prompt that creates a specification for a pacman game in a single html page. Consider edge cases and key implementation details that result in bugs. , execute prompt. It will often yield a much better result than one generic prompt. Now that models are trained on how to generate prompts for themselves this is quite productive.…
I thought this kind of chaining was already part of these systems.
It can be, but the more specific context you can give the better, especially on your initial prompting. If it is opaque to you who knows what it is doing. Dialing in the initial spec/prompt for 5 minutes is still important. Different LLMs and models will do better or worse on this and by being a human in the loop on this initial stuff my experience is much higher quality, which indicates to me, the LLM tries, but just doesn't always have enough info to implement your intentions in many cases yet.