[resource0x]

Examples of wrongness include most of arithmetic and logical inference (like in the example above). If you ask about the mass of 1 kilogram of nails, it gives the correct answer. The problem is that when the answer is wrong, it's not a "bug" that can be "fixed". It's just happens that, based on training data, the parameters of the resultant Rube Goldberg device are such that the weight of 1 kilogram of nails depends on the type of nails. It doesn't make sense even to ask why.

[AnimalMuppet]

So it fails in situations where there are precisely correct answers, and thrives in vagueness. I suppose that shouldn't surprise me.

You could think about coupling it with an inference engine, and letting the inference engine win if it can generate a result, and otherwise going with the ChatGPT output. That might fix it to some degree.