The prompt is not a magic incantation — it is a specification. What decades of writing good requirements teaches us about getting reliable output from language models.
Every failed software project shares a root cause: someone asked for the wrong thing precisely. Requirements engineering exists because humans are bad at stating what they want until they see what they asked for. Prompting reproduces this ancient problem at the speed of light.
A vague prompt returns plausible garbage in seconds; a precise spec returns working software. The model did not get smarter between those two attempts — the requirements did.
Good requirements name their inputs, outputs, constraints, and failure modes. Good prompts do exactly the same: the interface being built, the data available, the style constraints, and — most neglected — what should happen when things go wrong.
The discipline transfers directly. If you cannot describe the behavior unambiguously to a model, you could not have described it to a contractor either. The LLM is simply the first audience that reads your spec instantly and charges by the token.
Requirements evolve through review cycles; prompts evolve through eval loops. The professional practice is identical — encode examples, assert expected outcomes, and let failures refine the specification instead of patching individual outputs by hand.
Teams that bolt evals onto prompting ship systems that improve. Teams that hand-tune prompts ship demos.
Static generation, global replication, and functions at the edge used to be an enterprise luxury. Now it is the sensible default — here is the architecture playbook.