Large Language Models (LLMs) are scaled-up instances of Deep Neural Language Models—a type of Natural Language Processing (NLP) tools trained with Machine Learning (ML). To best understand how LLMs work, we must dive into what technologies they build on top of and what makes them different. To achieve this, an overview of the history of LLMs development, starting from the 1990s, is provided before covering the counterintuitive purely probabilistic nature of the Deep Neural Language Models, continuous token embedding spaces, recurrent neural networks-based models, what self-attention brought to the table, and finally, why scaling Deep Neural Language Models led to a qualitative change, warranting a new name for the technology.