with AI, documentation driven development is an understatement, if you take the time not just to document but to also provide lots of examples and potentially even data structures for the implementation (including intermediary data structures if you know them) the output is better than anything you would make in reasonable time.

▲ t-writescode 18 hours ago | parent [-]

If you have done or are doing all of that, why not just use the code you’ve made inside your docs?

Like, are you using languages where data structures are hard to write and/or work with? Typescript, Kotlin, Python and Ruby (via Sorbet or DryStruct) are all really easy to write all those data structures and code.

	▲	teravor 18 hours ago \| parent [-]
		what I meant was dictating the data structures for the code (transformations) the LLM is going to write. `"Show me your flowchart and conceal your tables, and I shall continue to be mystified. Show me your tables, and I won't usually need your flowcharts; they'll be obvious."` in my workflow I typically prompt the LLM to carefully consider if the data schema I provided it is not sufficient for whatever task I gave it and to then argue for including additional members, with GPT 5.5 I took notice because of the arguments it provided me, it became clear to me that it's over. they have 130+ IQ. it's just a matter of constructing scaffolds now to have them express the intelligence because due to whatever quirks of training they can do stupid things.