[buschleague]

This is exactly why enforcement needs to be architectural. The "challenges around maintainability and scalability" your clients hit exist because their AI workflows had zero structural constraints. The output quality problem isn't the model, it's the lack of workflow infrastructure around it.

[datsci_est_2015]

Is this not just “build a better prompt” in more words?

At what point do we realize that the best way to prompt is with formal language? I.e. a programming language?

[semiquaver]

No, the suite of linters, test suite and documentation in your codebase cannot be equated to “a better prompt” except in the sense that all feedback of any kind is part of what the model uses to make decisions about how to act.

[datsci_est_2015]

A properly set up and maintained codebase is the core duty of a software engineer. Sounds like the great-grandparent comment’s client needed a software engineer.

[oblio]

What if LLMs, at the end of the day are machines, so for now generally dumber than humans and the best they can provide are at most statistically median implementantions (and if 80% of code out there is crap, the median will be low)?

Now that's a scary thought that basically goes against "1 trillion dollars can't be wrong".

Now, LLMs are probably great range extenders, but they're not wonder weapons.

[lossyalgo]

Also who is to say what is actually crap? Writing great code is completely dependent on context. An AI could exclusively be trained on the most beautiful and clean code in the world, yet if it chooses the wrong paradigm in the wrong context, it doesn't matter how beautiful that code is - it's still gonna be totally broken code.