That prompts aren't science means little. If anything it makes them more important because you can't systematically arrive at good ones.
If one spends a lot of time building an application to achieve an actual goal they'll realize the prompts make a gigantic difference and it takes an enormous amount of fiddly, annoying work to improve. I do this (and I built an agent system, which was more straightforward to do...) in financial markets. It so much so that people build systems just to be able to iterate on prompts (https://www.promptlayer.com/).
I may be wrong - but I'll speculate you work on infra and have never had to build a (real) application that is trying to achieve a business outcome. I expect if you did, you'd know how much (non sexy) work is involved on prompting that is hard to replicate.
Hell, papers get published that are just about prompting!
If one spends a lot of time building an application to achieve an actual goal they'll realize the prompts make a gigantic difference and it takes an enormous amount of fiddly, annoying work to improve. I do this (and I built an agent system, which was more straightforward to do...) in financial markets. It so much so that people build systems just to be able to iterate on prompts (https://www.promptlayer.com/).
I may be wrong - but I'll speculate you work on infra and have never had to build a (real) application that is trying to achieve a business outcome. I expect if you did, you'd know how much (non sexy) work is involved on prompting that is hard to replicate.
Hell, papers get published that are just about prompting!
https://arxiv.org/abs/2201.11903
This line of thought effectively led to Gpt-4-o1. Good prompts -> good output -> good training data -> good model.