It means the same thing to you, but not to the whole spectrum of people using AI. You literally see it on Reddit all the time where people are complaining about the same model either over-engineering or doing too much, vs it being requiring too much steering or not being autonomous or capable enough to hand off tasks to on its own.

The reason prompting it to review its own work for loose ends, record any new undocumented or noteworthy behavior, suggest changes to tests/processes to make it go more smoothly the next time, etc is that it’s prescriptive and process-oriented (and thus easily verifiable/done in-context) rather than descriptive and outcome oriented (which to do properly could require way more context than the model has, because it doesn’t know what it doesn’t know about your particular work, only what it’s seen so far).

Even promoting it to do these after-the-fact vs as an upfront requirement can have a big impact IMO. If you make “maintainability” part of the task before it’s seen the real work it will focus on general “best practices” crap rather than the real work, so either way if this is something you care about it doing you have to give it guidance for how you want it done.

If you were to review the logs of a model after the fact, you’d also not really save on input tokens unless you compressed the context or sharded it out, which can easily miss the small details that constitute the difference between “what actually happened” vs “how the LLM models this general class of problems” unless the first pass involves the entire context anyway. That said I do think there’s a lot of value in building some kind of pipeline for validating and aggregating these “learnings” across sessions.