Don't claim it hasn't gotten dumber. It's easy to find excuses, but none that explain my experience.
That is not an excuse, and explains your experience.
Doesn't matter how many times I try it now, it just doesn't work.
I'm not! What you describe is perfectly normal. Of course you'd need multiple tries to get the "right" output and of course if you keep trying you'll get different results. It's a stochastic process and you have very limited means to control the output.
If you sample from a model you can expect to get a distribution of results, rather tautologically. Until you've drawn a large enough number of samples there's no way to tell what is a representative sample, and what should surprise you. But what is a "large enough" number of samples is hard to tell, given a large model, like a large language model.
>> Doesn't matter how many times I try it now, it just doesn't work.
Wanna bet? If you try long enough you'll eventually get results very similar to the ones you got originally. But it might take time. Or not. Either you got lucky the first few times and saw uncommon results, or you're getting unlucky now and seeing uncommon results. It's hard to know until you've spent a lot of time and systematically test the model.
Statistics is a bitch. Not least because you need to draw lots of samples and you never know how close you are to the true distribution.
I agree with this. The analogy I use on repeat is the dawn of moving picture making. The first movies were short larks, designed just to elicit a response. Just like when CGI was new- a bunch of over-the-top, sensationalist fluff got made. This tech needs to mature. And we need it to continue to be fed the work of real humans, not AI feeding on AI, a recent phenomenon that hopefully will not turn out to be the norm. If we water and feed it responsibly, it will only grow more capable and useful over time.