> It will point out things that are unclear, etc. You can go far beyond just micro managing incremental edits to some thing.
When prompted an LLM will also point it out when it's perfectly clear. LLM is just text prediction, not magic
Yes, indeed.
But:
Why can LLMs generally write code that even compiles?
While I wouldn't trust current setups, there's no obvious reason why even a mere LLM cannot be used to explore the design space when the output can be simulated to test its suitability as a solution — even in physical systems, this is already done with non-verbal genetic algorithms.
> LLM is just text prediction, not magic
"Sufficiently advanced technology is indistinguishable from magic".
Saying "just text prediction" understates how big a deal that is.
Having to test every assertation sounds like a not particularly useful application, and the more variables there are the more it seems to be about throwing completely random things at the wall and hoping it works
You should use a tool for it's purpose, relying on text prediction to predict clarity is like relying on teams icons being green to actual productivity; a very vague, incidentally sometimes coinciding factor.
You could use text predictor for things that rely on "how would this sentence usually complete" and get right answers. But that is a very narrow field, I can mostly imagine entertainment benefiting a lot.
You could misuse text predictor for things like "is this <symptom> alarming?" and get a response that is statistically likely in the training material, but could be completely inverse for the person asking, again having very high cost for failing to do what it was never meant to. You can often demonstrate the trap by re-rolling your answer for any question a couple times and seeing how the answer often varies mild-to-completely-reverse depending on whatever seed you land.
That should be fully automated.
Instead of anchoring on "how do I test what ChatGPT gives me?", think "Pretend I'm Ansys Inc.*, how would I build a platform that combines an LLM to figure out what to make in the first place from a user request, with all our existing suite of simulation systems, to design a product that not only actually meets the requirements of that user request, but also actually proves it will meet those requirements?"
* Real company which does real sim software
Here on HN we often see posts insisting on the importance of "first principles".
Your embrace of "magic" - an unknown black box who does seemingly wonderful things that usually blow up to one's face and have a hidden cost - is the opposite of that.
LLMs are just text prediction. That's what they are.
>Why can LLMs generally write code that even compiles?
Why can I copy-paste code and it compiles?
Try to use LLM on code there is little training material about - for example PowerQuery or Excel - and you will see it bullshit and fail - even Microsoft's own LLM.
I think phrasing it like that is called "begging the question": you've already skipped past all the intelligence you had to apply to figure out which part of the entire internet constituted "code".
And not just any code, but code in correct language. If I copy-paste C64 Basic into the middle of a .swift file (and not as a string), it isn't going to compile.
And not just in the correct language, but a complete block of it, rather than a fragment.
> even Microsoft's own LLM.
"even" suggests you hold them in higher regard than I do.
This sort of glib talking point really doesn't pass muster, because if you showed the current state of affairs to a random developer from 2015, you would absolutely blow their damned socks off.
That's the very root cause why we still have unsolved problems like the inability to get the same answer to the same questions, the inability to do riguorous maths or logic (any question that only has one good answer, in fact) and hallucinations!
I suspect it will be more of a tool calling layer that is building up the model based on the output of more deterministic tools. "I need this bearing mounted here" -> LLM reads the part number of the bearing, does a RAG search for the bearing properties to get sizes, looks up what type of fits should be used in the scenario, feeds that info into a function that generates the appropriate seat geometry. Rather than trying to morph triangles into sort-of-kind-of a cylindrical face that may or may have the 0.0XXmm interference that is the difference between success or failure of the fitment.