In a previous article, we explored modelling the "mind" of large language models (LLMs) and how they process information. As debates continue about artificial general intelligence and whether LLMs could ever be truly sentient, it is important to dive deeper into understanding their core functions.
How do these artificial neural networks actually think and reason? What are the implications for properly steering their capabilities through prompt engineering?
This article will break down the fundamental nature of LLMs as next token predictors. Grasping this concept is key to utilizing them effectively and pushing AI advancements forward. By better understanding the mechanisms
