Large Language Models (LLMs) like ChatGPT can handle two aspects of communication very well: plausibility and fluency. Given an input context they determine what are the most probable sequence of words and string them in a way that is superbly eloquent. That makes the output very convincing. But it's no secret that LLMs can provide entirely false outputs - they can confabulate. Not hallucinate...
As explained in this Stanford CS50 tech talk, Language Models (LMs) are basically a probability distribution over some vocabulary. For every word we give an LM, it can determine what the most probable word to come after that. It's trained to predict the Nth word, given the previous N-1 words. If that sounds like simple probability calculation, you are not realizing that predicting the next word...
This seemed like the core ideas so I wanted to clarify them conceptually. "Embeddings" emphasizes the notion of representing data in a meaningful and structured way, while "vectors" refers to the numerical representation itself. 'Vector embeddings' is a way to represent different data types (like words, sentences, articles etc) as points in a multidimensional space. Somewhat regrettably, both...