It's also important to handle cases where the word patterns (or token patterns, rather) have a negative correlation with the patterns in reality. There are some domains where the majority of content on the internet is actually just wrong, or where different approaches lead to contradictory conclusions.

E.g. syllogistic arguments based on linguistic semantics can lead you deeply astray if you those arguments don't properly measure and quantify at each step.

I ran into this in a somewhat trivial case recently, trying to get ChatGPT to tell me if washing mushrooms ever really actually matters practically in cooking (anyone who cooks and has tested knows, in fact, a quick wash has basically no impact ever for any conceivable cooking method, except if you wash e.g. after cutting and are immediately serving them raw).

Until I forced it to cite respectable sources, it just repeated the usual (false) advice about not washing (i.e. most of the training data is wrong and repeats a myth), and it even gave absolute nonsense arguments about water percentages and thermal energy required for evaporating even small amounts of surface water as pushback (i.e. using theory that just isn't relevant when you actually properly quantify). It also made up stuff about surface moisture interfering with breading (when all competent breading has a dredging step that actually won't work if the surface is bone dry anyway...), and only after a lot of prompts and demands to only make claims supported by reputable sources, did it finally find McGee's and Kenji Lopez's actual empirical tests showing that it just doesn't matter practically.

So because the training data is utterly polluted for cooking, and since it has no ACTUAL understanding or model of how things in cooking actually work, and since physics and chemistry are actually not very useful when it comes to the messy reality of cooking, LLMs really fail quite horribly at producing useful info for cooking.