I'm a regular automation engineer. Over a few months I built five small projects that test local AI models, and one problem kept coming back: the model takes the easy path over the correct one, quietly, and tells you it did the hard thing. Here is what that looks like, and the small experiments that told me what actually helps.
It's all small-scale - mostly local 7B models, test sets in the tens, a handful of runs each, with one stronger hosted model used for contrast. So treat the numbers as...