The researchers in the academic field of machine learning who came up with LLMs are certainly aware of their limitations and are exploring other possibilities, but unfortunately what happened in industry is that people noticed that one particular approach was good enough to look impressive and then everyone jumped on that bandwagon.
That’s not the problem though. Because if I apply my perspective I see this:
Someone took a shortcut because of an external time-crunch, left a comment about how this is a bad idea and how we should reimplement this properly later.
But the code worked and was deployed in a production environment despite the warning, and at that specific point it transformed from being “abstract procedural logic” to being “business logic”.
The researchers in the academic field of machine learning who came up with LLMs are certainly aware of their limitations and are exploring other possibilities, but unfortunately what happened in industry is that people noticed that one particular approach was good enough to look impressive and then everyone jumped on that bandwagon.
That’s not the problem though. Because if I apply my perspective I see this:
Someone took a shortcut because of an external time-crunch, left a comment about how this is a bad idea and how we should reimplement this properly later.
But the code worked and was deployed in a production environment despite the warning, and at that specific point it transformed from being “abstract procedural logic” to being “business logic”.