Thank you for putting into words how a lot of AI prose makes me feel. 'Persistent mic drops' is the most accurate way I have seen it described and I would even argue the 'this-not-that writing' is part of it. Setting up a chain of ever increasing seemingly powerful phrases to then conclude in a final statement which may or may not actually explain the core concept.
It also made me think of the Feynman Learning Technique[1] or learning by teaching in general: being able to accurately explain in simple terms requires to fully grasp the concept and noticing gaps in the explanation is an insight to the explainer to evaluate and understand those parts again at a deeper level. An AI arguably does not have this understanding and will happily drive home any point we have asked it to make.
In a way I think it's tied to the other discussion from today[2] where it was posited that domain knowledge makes you better at prompting LLMs and, consequently, would enable you to also better structure and evaluate explainers. But then again, explaining well is a different skill altogether.
It also made me think of the Feynman Learning Technique[1] or learning by teaching in general: being able to accurately explain in simple terms requires to fully grasp the concept and noticing gaps in the explanation is an insight to the explainer to evaluate and understand those parts again at a deeper level. An AI arguably does not have this understanding and will happily drive home any point we have asked it to make.
In a way I think it's tied to the other discussion from today[2] where it was posited that domain knowledge makes you better at prompting LLMs and, consequently, would enable you to also better structure and evaluate explainers. But then again, explaining well is a different skill altogether.
[1]: https://en.wikipedia.org/wiki/Learning_by_teaching#Plastic_p... [2]: https://hackertimes.com/item?id=49161518