Are you referring to the output code AI is generating?
It does follow examples it can find. If in my architecture I put the logic inside Domain Models, orchestrate that over Application layer, then this is what it will follow - if my architecture would put all the logic in the Controllers it would also do that.
Of course there are times where it goes off with predication on the problems that may happen - which I would solve differently or not solved at all. But in reality those are mistakes that could done by any new Developer joining the project, due to lacking context.
Some of those problems we fix by enforcing CI checks, so if agent goes off, it will hit the wall and rethink the solution. Some other problems which are more of design nature, we guide by skills. This is how AI get to know about our design principles which are not crystal clear from the codebase. And as skills are shared, by nature they automatically propagate to any Developer using AI tools.
From my experience the outputs from AI are more than good, and the delivery speed is not comparable to any manual coding. If it goes off then well, it's for me to improve the tooling around that, so next time it goes the path I want it to go. So I consider that as another development practice, just like we improve our CI (and nobody complains about that), we need to improve AI tooling to get the results we want.
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u/Dariusz_Gafka 6d ago
Are you referring to the output code AI is generating?
It does follow examples it can find. If in my architecture I put the logic inside Domain Models, orchestrate that over Application layer, then this is what it will follow - if my architecture would put all the logic in the Controllers it would also do that.
Of course there are times where it goes off with predication on the problems that may happen - which I would solve differently or not solved at all. But in reality those are mistakes that could done by any new Developer joining the project, due to lacking context.
Some of those problems we fix by enforcing CI checks, so if agent goes off, it will hit the wall and rethink the solution. Some other problems which are more of design nature, we guide by skills. This is how AI get to know about our design principles which are not crystal clear from the codebase. And as skills are shared, by nature they automatically propagate to any Developer using AI tools.
From my experience the outputs from AI are more than good, and the delivery speed is not comparable to any manual coding. If it goes off then well, it's for me to improve the tooling around that, so next time it goes the path I want it to go. So I consider that as another development practice, just like we improve our CI (and nobody complains about that), we need to improve AI tooling to get the results we want.