Some things are inherent in the way the current LLM’s work. It doesn’t reason, it doesn’t understand, it just predicts the next word out of likely candidates based on the previous words. It can’t look ahead to know if it’s got an answer, and it can’t backtrack to change previous words if it later finds out it’s written itself into a corner. It won’t even know it’s written itself into a corner, it will just continue predicting in the pattern it’s seen, even if it makes little or no sense for a human.

It just mimics the source data it’s been trained on, following the patterns it’s learned there. At no point does it have any sort of understanding of what it’s saying. In some ways it’s similar to this, where a man learned how enough french words were written to win the national scrabble competition, without any clue what the words actually mean.

And until we get a new approach to LLM’s, we can only improve it by adding more training data and more layers allowing it to pick out more subtle patterns in larger amounts of data. But with the current approach, you can’t guarantee that what it writes will be correct, or even make sense.