Generative AI and Large Language Models (LLMs) for Beginners

Generative Artificial Intelligence is transforming how we build software, analyze data, and solve complex problems. This text-based course offers a clear, accessible entry point into the world of Large Language Models (LLMs), designed specifically for those who want to understand the technology behind the hype. You will transition from a curious learner to a knowledgeable practitioner capable of discussing, selecting, and applying LLMs to real-world scenarios. By exploring the core mechanics of these models, you will gain the confidence to design effective prompts, understand training lifecycles, and integrate modern AI patterns into your workflows. What you'll learn: - Understand the core transformer architecture that powers modern generative models - Navigate the complete generative AI lifecycle from data collection to deployment - Explore model fine-tuning techniques, including parameter-efficient fine-tuning (PEFT) - Apply prompt engineering best practices to guide model outputs effectively - Grasp the fundamentals of Retrieval-Augmented Generation (RAG) and vector databases - Evaluate model performance and discuss ethical challenges, bias, and alignment The course begins with essential terminology, basic concepts, and foundational definitions of generative AI. From there, you will read through step-by-step written explanations of the transformer architecture, training methodologies, and modern system architectures like RAG, complete with clear code snippets and conceptual walkthroughs. This course is designed for software developers, product managers, data analysts, and tech enthusiasts who are new to generative AI and want a comprehensive, non-intimidating introduction without complex mathematical prerequisites. Start reading today to unlock the potential of large language models and build your foundational AI skillset.