Depends on the prior state.
For one codebase I work in, LLM written code is noticeably and measurably (we have literally measured it for bugs, performance, etc.) better than what existed before. So the LLM code is a welcome change.
For one codebase I work in, LLM written code is noticeably and measurably (we have literally measured it for bugs, performance, etc.) better than what existed before. So the LLM code is a welcome change.
One of my open questions is how much that increases over time. It could be that is a constant. But it also could be that, say, coding agents will infer false needs from the excess code/docs and elaborate further.
LLM code that is written to a lower standard has not had the controls put in place.
My take is that the main difference is the approach to problems the coding agents have. They optimize towards presuming a fully working invocation environment, yet checking everything anyway, and then rolling back any changes and re-testing that such changes worked. Which is great for normal types of software but incredibly tedious for anything aimed at less than a fully hands free automated environment.
In other words, AI is very awesome at scope creep of assigned problems and targeting the validation prompts baked into the review system.
Also, regarding your accusation, you need to contextualize- what language, what use-case, what LLM, etc.?
It'll mention the old version then the change, and the relating tickets.
It encodes context it needs into doc comments and regular comments that make them in insufferable to read and so verbose because the comments contain info on other parts of the code base (that might have changed since that comment was written) but were useful to the model for its implementation. I have so far found no way to stop claude from doing this. It will sometimes do it with hand holding but the moment a task takes a slightly large amount of context its back to the word barf.
We basically took our documented (human) SDLC process and applied it to the relevant harness hooks. Since our SDLC docs talked about what good comments looked like and what to avoid, we basically had the direction for the tools. Opus 5 did throw us a loop and increase the verbosity and decrease usefulness of the text, but refining our documentation cleared that hurdle within about a day.