don’t run any commands that you don’t understand. Ask it to break down any commands it tells you to run if you don’t understand them.
You need to pay extra attention to this, as ML models will spit out commands and parameters that doesn’t exists if there was not enough examples in training dataset for that action. Especially with explain as it could just spit out totally wrong but “sounding good” explanation for parameter etc as it not always will tell the magic keywords like “typically” that indicate that it doesn’t have confidence as it’s “based on other similar command/knowledge”.
In your example it spit out:
<span style="color:#323232;"> -m: Prune empty directory chains from the file-list.
</span><span style="color:#323232;"> --prune-empty-dirs: Exclude empty directories that result from the inclusion/exclusion pattern.
</span>
which is actually exactly the same parameter with 2 different explanations, you can confirm this with man rsync
So the more edge case you have the bigger chance it will spill out bad results, but those new models are shockingly good especially for very common use cases.