Lesson 1: The Foundations of Large Language Models

The Architecture of ThoughtAt its core, a [Large Language Model] is a probability engine. It does not think in the way humans do; instead, it predicts the next sequence of words based on a massive training dataset. This process relies on the [Transformer] architecture, first introduced by researchers at Google, which uses an attention mechanism to weigh the importance of different words in a sentence regardless of their distance from one another. Understanding this ensures you treat the model as a statistical tool rather than a sentient entity. The model processes data through [Tokens], which are the fundamental units of text the AI understands. A token can be a whole word, a part of a word, or even a punctuation mark. When you send a prompt, the system tokenizes your input, converts these tokens into numerical vectors within a multidimensional space, and processes them through layers of neural networks to generate an output. To learn more about how these architectures are structured, you should review the foundational paper on Attention Is All You Need. Furthermore, understanding how [Machine Learning] training phases like Supervised Fine-Tuning (SFT) and Reinforcement Learning from Human Feedback (RLHF) shape the model's behavior is vital for effective interaction. Consult the OpenAI API documentation to see how these models are exposed to developers. For broader context on machine learning, see Google's Machine Learning Glossary. FAQsIs an LLM actually conscious?No, an LLM is a complex statistical model that predicts text based on patterns. It has no internal experience or subjective awareness. What is a token exactly?A token is the piece of text the AI processes. Generally, one token equals about 0.75 words in English. Why do models sometimes get facts wrong?This is called a hallucination, which happens when the model prioritizes fluent, plausible-sounding text over factual accuracy based on its training weights.