But the absence of papers is precisely the problem and why all this LLM stuff has become a new religion in the tech sphere.

Either you have faith and every post like this fills you with fervor and pious excitement for the latest miracles performed by machine gods.

Or you are a nonbeliever and each of these posts is yet another false miracle you can chalk up to baseless enthusiasm.

Without proper empirical method, we simply do not know.

What's even funnier about it is that large-scale empirical testing is actually necessary in the first place to verify that a stochastic processes is even doing what you want (at least on average). But the tech community has become such a brainless atmosphere totally absorbed by anecdata and marketing hype that no one simply seems to care anymore. It's quite literally devolved into the religious ceremony of performing the rain dance (use AI) because we said so.

One thing the papers help provide is basic understanding and consistent terminology, even when the models change. You may not find value in them but I assure you that the actual building of models and product improvements around them is highly dependent on the continual production of scientific research in machine learning, including experiments around applications of llms. The literature covers many prompting techniques well, and in a scientific fashion, and many of these have been adopted directly in products (chain of thought, to name one big example—part of the reason people integrate it is not because of some "fingers crossed guys, worked on my query" but because researchers have produced actual statistically significant results on benchmarks using the technique) To be a bit harsh, I find your very dismissal of the literature here in favor of hype-drenched blog posts soaked in ridiculous language and fantastical incantations to be precisely symptomatic of the brain rot the LLM craze has produced in the technical community.