LLM Architecture and Data Preparation for Generative AI

Large language models are reshaping the technological landscape, but the secret to their success lies in their internal design and the quality of the data they consume. This course provides a clear, text-based path for anyone looking to understand how these complex systems are built and how to prepare information for AI processing. You will move from foundational concepts to the practical logic behind the most advanced models used today. By the end of this course, you will have a solid grasp of the architectural choices that define generative AI and the data pipelines that make them functional. You will be able to explain how models learn patterns and what steps are necessary to ensure data is ready for training or fine-tuning. What you'll learn: - Understand the evolution from basic neural networks to modern Transformer-based architectures. - Learn the mechanics of self-attention and how models process and generate human-like text. - Practice data cleaning, normalization, and tokenization techniques for large-scale datasets. - Explore the role of vector databases and retrieval-augmented generation (RAG) in current AI workflows. - Differentiate between various model types, including GANs, RNNs, and diffusion models. - Apply prompt engineering basics to better control and refine model outputs. The course begins with a thorough introduction to AI terminology and the history of sequence modeling before moving into the specifics of modern model design and data preparation strategies. This structured approach ensures you build a strong theoretical foundation before exploring how these systems are implemented in real-world scenarios. This course is designed for beginners, data enthusiasts, and aspiring engineers who want to understand the inner workings of generative AI without needing prior experience in the field. Start building your foundational knowledge of generative AI architecture today.