LLM Architecture and Data Preparation for Generative AI

Building powerful Generative AI applications requires more than just calling an API; it demands a solid understanding of how Large Language Models (LLMs) are structured and how data is prepared to train them. This text-based course guides you through the foundational concepts of LLM architectures and the critical data preparation pipelines that fuel them. You will transition from understanding basic transformer mechanics to writing Python code that tokenizes, cleans, and structures raw text for model training. What you'll learn: - Understand the fundamental architecture of transformers, attention mechanisms, and LLMs. - Prepare and preprocess raw text data using modern Python libraries and tokenization techniques. - Configure data pipelines that feed clean, structured datasets into neural networks. - Explore the basics of PyTorch for managing model inputs and embeddings. - Integrate vector databases and retrieval-augmented generation (RAG) concepts into your data workflow. - Apply best practices for evaluating data quality to avoid bias and improve model performance. We begin with the core terminology of deep learning and neural networks before moving step-by-step through transformer architecture, tokenization strategies, and practical data pipeline implementation. This course is designed for aspiring data professionals, software developers, and AI enthusiasts; while a basic familiarity with Python is helpful, no prior experience with machine learning is required to begin. Start reading today to build a strong foundation in the architecture and data engineering principles behind modern generative AI.