How Transformers Work: The Architecture of Large Language Models

Ever wondered what actually happens inside a Large Language Model when it generates text? Understanding the underlying transformer architecture is the key to unlocking the full potential of modern AI. This text-based course guides you through the inner workings of modern language models. By learning how these systems process, analyze, and generate human language, you will gain the foundational knowledge needed to write highly effective prompts, debug AI behaviors, and integrate language models into your projects with confidence. What you'll learn: - Understand the foundational concepts of tokenization, embeddings, and vector representations of text. - Explore the self-attention mechanism to see how models weigh the relationships between words in a sentence. - Analyze the encoder-decoder structure and see how modern decoder-only architectures generate text. - Learn how pre-training and fine-tuning shape a model's capabilities and safety alignments. - Apply structural knowledge to optimize prompt engineering and control model output predictability. - Discover how modern retrieval-augmented generation (RAG) systems connect external data to transformer models. You will start with core terminology and the conceptual intuition behind text representation before exploring attention mechanisms, model training phases, and practical optimization strategies. This course is designed for curious beginners, developers, and product managers who want to understand the technology behind AI without needing a background in advanced mathematics. Start reading today to demystify the technology powering the modern AI revolution.