Deconstructing the Transformer: LLMs from Embeddings to Attention

Are you using Large Language Models (LLMs) but feel limited by only understanding their inputs and outputs? To truly harness the power of generative AI, developers must understand the underlying mechanisms that drive these powerful tools. This course provides a practical, concept-driven breakdown of the Transformer architecture, the engine behind all modern LLMs. By the end, you will transition from a consumer of LLMs to a developer who understands their inner workings, allowing for smarter application development and troubleshooting. What you'll learn: * Understand the mathematical foundations of word embeddings and tokenization processes. * Master the concept of the Attention mechanism and how it processes sequential dependencies in data. * Apply knowledge of the Encoder and Decoder stacks within the full Transformer architecture. * Learn how positional encoding enables models to process sequential information effectively. * Practice effective Prompt Engineering techniques for reliably controlling model output and behavior. * Configure basic Retrieval-Augmented Generation (RAG) patterns for grounding LLMs in external data sources. We begin by defining essential terminology and exploring how natural language is converted into usable vectors. We then systematically unpack the core components of the Transformer, culminating in practical strategies for interacting with and deploying modern LLMs. This course is designed for developers, engineers, and technical enthusiasts who are new to the internal mechanics of generative AI. No prior deep learning experience is required; we start with foundational concepts. Start building a robust technical foundation for working with Large Language Models today.