However, this only works if you know a better architecture exists. Most won't and they'll use the "defaults". And these defaults is what the next model trains on. Individually you can steer, but I'm concerned it won't be enough to steer the critical mass of the training data.
Just create your own guardrails. Lint rules. Hooks. Whatever. AI also keeps training on newer data. We move on.
And if they are, how many are keeping this up to date?
How many are following any best practices? Whose problem is that? Not AI.
> And if they are, how many are keeping this up to date?
And how many projects use the latest Spring (mentioned in the article)?
Don't blame AI. Blame the humans.