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This is probably because of a lack of training data, where it is referencing only one example and that example just had a mistake in it.
The one example could be flawless, but the output of an LLM is influenced by all of its input. 99.999% of that input is irrelevant to your situation, so of course it’s going to degenerate the output.
What you (and everyone else) needs is a good search engine to find the needle in the haystack of human knowledge, you don’t need that haystack ground down to dust to give you a needle-shaped piece of crap with slightly more iron than average.
You can do either, but you usually do neither. The best way is to throw a new exception for your situation and add the caught exception as an inner exception. Because rethrowing resets the stack trace, removing the context from an exception message that is often pretty vague, and “bouncing” with
throw;
doesn’t tell the next exception handler that you already handled (part of) the exception.