Tutorial builds LLM from scratch: turns out the 'magic' is just steroid-fueled autocomplete pretending to think

A recent article series, "Fazendo um LLM do Zero", aims to demystify the magic behind Large Language Models (LLMs) by explaining the statistical concepts that power them. The first installment of the series reveals that LLMs, such as the GPT-4, do not possess consciousness or understanding, but rather predict the next word in a sequence based on probability calculations. This is achieved through the Transformer architecture, introduced by Google in 2017, which enables parallel processing of entire text sequences. The GPT model, a type of Decoder-Only Transformer, is specifically designed for generating text and excels at tasks such as conversation and storytelling. By understanding the statistical nature of LLMs, developers and users can improve their interaction with these models, moving from attempting to "convince" the chat to "engineering" the prompt. The series will continue to explore the technical aspects of LLMs, including the mathematics behind probability calculations.