> LLMs can clean up after themselves if you steer them. It's not very hard. I often have a two-step dance I do where the LLM first outputs some code and then I prompt it to fix the types up to my standards.

My experience has been the opposite. I prompt it and it generates something that mostly works, but steering it into something that would actually be maintainable is an exercise in futility. GPT-5.5 uses up my entire allocation of tokens in just reading the context docs and doing a single shot task.

> aven't found a way to prompt it with any number of skills or CLAUDE.mds or anything else to get it to do it the way I want on the first pass, but it's not that hard to just fix it afterwards.

I've yet to find a way to get an LLM to actually follow the instructions in CLAUDE/Agents.md It very quickly gets to a point where it forgets explicit instructions such as "run clang-format on all .h/.cpp files that you touch", and saying "our coding standards can be found at <link to Notion/Confluence>, please ensure all code adheres strictly to this standard" is ignored. I'd also say that the first pass of the code is very often not even close to how it _should_ be implemented so it's not just review and patch up, it's rearchitect + restructure 50% of the code.

> it also rapidly starts changing things just to change things,

Agreed. LLM's are (very good) text generators. They are good for generating code, and left to their own devices they will generate and generate and generate. Getting them to edit, simplify, and foresee future problems is something that people keep saying "use the latest model, it's amazing (despite saying that about the last 3 models" or "you just need to use <harness|framework>" or "your agents.md needs to contain XYZ" will solve.

"My experience has been the opposite. I prompt it and it generates something that mostly works, but steering it into something that would actually be maintainable is an exercise in futility."

I've seen others have this experience, too.

It would be interesting to sit us down next to each other for a day or two and compare how we do things, but I suspect much less than that won't reveal much of interest.

It is weird to me how often I have to remind the models about their skills or CLAUDE.md (or equivalent). Though I've sort of taken it as just another way I can impact the process, because sometimes I'm happy for them to forget a particular skill for a moment... particularly when I want it to just do a thing based on my prompt and it decides to invoke the "OK let's design this super carefully" skill. I've gotten a lot of use out of that one but I'm pretty comfortable having to explicitly invoke it.

> I've yet to find a way to get an LLM to actually follow the instructions in CLAUDE/Agents.md

I'll give you a hint: do you rely on people to do the right things, or do you have automated unit, integration, and e2e testing? Do you have linters? Do you have static analysis that automatically runs and will block when violated?

If you are verifying humans, why aren't you verifying LLMs?