The force-feeding of AI features on an unwilling public

It's like talking into a void. The issue with AI is that it is too subtle, too easy to get acceptable junk answers and too subtle for the majority to realize we've made a universal crib sheet, software developers included, perhaps one of the worst populations due to their extremely weak communications as a community. To be repeatedly successful with AI, one has to exert mental effort to prompt AI effectively, but pretty much nobody is willing to even consider that. Attempts to discuss the language aspects of using an LLM get ridiculed as 'prompt engineer is not engineering' and dismissed, while that is exactly what it is: prompt engineering using a new software language, natural language, that the industry refuses to take seriously, but is in fact an extremely technical programming language so subtle few to none of you realize it, nor the power that is embodied by it within LLMs. They are incredible, they are subtle, to the degree the majority think they are fraud.

Isn't "Engineering" is based on predictability, on repeatability?

LLMs are not very predictable. And that's not just true for the output. Each change to the model impacts how it parses and computes the input. For someone claiming to be a "Prompt Engineer", this cannot work. There are so many variables that are simply unknown to the casual user: training methods, the training set, biases, ...

If I get the feeling I am creating good prompts for Gemini 2.5 Pro, the next version might render those prompts useless. And that might get even worse with dynamic, "self-improving" models.

So when we talk about "Vibe coding", aren't we just doing "Vibe prompting", too?

> LLMs are not very predictable. And that's not just true for the output.

If you run an open source model from the same seed on the same hardware they are completely deterministic. It will spit out the same answer every time. So it’s not an issue with the technology and there’s nothing stopping you from writing repeatable prompts and promoting techniques.

> If you run an open source model from the same seed on the same hardware they are completely deterministic.

Are you sure of that? Parallel scatter/gather operations may still be at the mercy of scheduling variances, due to some forms of computer math not being associative.