EP1: Transformer & The Power of Scale

A breakdown of modern LLM architecture tailored for software engineers. We explore how the Transformer revolutionized AI by processing text in parallel to replace older, sequential RNNs. We also demystify the "self-attention" mechanism, explaining how it uses Queries, Keys, and Values much like an information retrieval system to build deep contextual understanding. Finally, we dive into empirical Scaling Laws, revealing how the massive scale of models like the 175-billion parameter GPT-3 unlocked "in-context learning"—the emergent ability to perform completely new tasks on the fly just by reading a prompt, without requiring any underlying parameter updates or fine-tuning