This course beings with the evolution of modern Natural Language Processing (NLP) and the significant advancements made in this technology over the past few decades. You'll delve into the mechanics of contemporary deep learning architectures, addressing questions such as how machines learn to read and write text and covering the key topics of attention and alignment mechanisms. Next, you'll explore large language models (LLMs) like ChatGPT, Llama, and T5, and learn about their underlying mechanisms. Building on this foundational knowledge, you'll transition to practical applications, such as performing semantic searches across extensive databases and building retrieval augmented generation (RAG) systems using both closed and open-source components. You will also learn how to craft effective prompts for LLMs. The course culminates in your constructing basic RAG systems and developing an AI agent capable of executing multiple tasks sequentially in a natural conversational manner. This module serves as your introduction to the expansive world of LLMs, catering to both beginners and enthusiasts eager to deepen their understanding.