The Transformer does exactly that—it teaches neural networks which words to focus on and how they relate to each other, enabling the AI to understand context with unprecedented clarity. This breakthrough led to the development of GPT, and the progression has been staggering. From GPT-1 with just 100 million parameters to GPT-3 with 175 billion parameters, we've witnessed an exponential explosion in model size and capability. Modern language models now contain hundreds of billions of parameters, each one representing a learned pattern about language. The second crucial innovation was the combination of pretraining and fine-tuning, which works like a student's education. First comes the pretraining phase—like reading thousands of books and learning the fundamentals of language. The model ingests vast amounts of text from the internet, learning grammar, facts, reasoning patterns, and how to structure thoughts. Then comes fine-tuning, the practical application phase—like practicing conversations with a tutor. Through techniques like reinforcement learning from human feedback, the model learns to generate helpful, harmless, and honest responses. This two-stage approach created the foundation for models that could engage in natural, coherent dialogue with humans.