So, this is what I have been thinking (and part of the reason why I want to "cook" it more before shipping). Since I have been building it for so long, I am not sure how much "overfit" it is on my data, the way I speak and the phrases I 'm using.

And of course the opposite too, how much the dog trained me to speak to it a certain way to maximize outcome success.

But then I thought, when people play games they are not using highly sophisticated vocabulary and there is probably lots of repetition since they are always under some form of multi-tasking stress (playing and replying/speaking). So maybe... maybe, the system can adjust itself. Use a big LLM offline to say "user said X, we did Y - was that good?" - then retrain itself.

The decomposer is basically a bunch of old-school embeddings/classifiers stitched together, it can train super fast and doesn't need tons of data. Could the thing calibrate itself to the user? Does it even need to? (because as I said I 'm a datapoint of 1 and I am not ready for the potentially huge stream of bug reports when I ship (add some perfectionism to the mix and you get the idea)).

edit: typos