Author Description

<p>Explore reusable design patterns, including data-centric approaches, model development, model fine-tuning, and RAG for LLM application development and advanced prompting techniques</p><p>Key Features: </p><p>- Learn comprehensive LLM development, including data prep, training pipelines, and optimization</p><p>- Explore advanced prompting techniques, such as chain-of-thought, tree-of-thought, RAG, and AI agents</p><p>- Implement evaluation metrics, interpretability, and bias detection for fair, reliable models</p><p>- Print or Kindle purchase includes a free PDF eBook</p><p>Book Description: </p><p>This practical guide for AI professionals enables you to build on the power of design patterns to develop robust, scalable, and efficient large language models (LLMs). Written by a global AI expert and popular author driving standards and innovation in Generative AI, security, and strategy, this book covers the end-to-end lifecycle of LLM development and introduces reusable architectural and engineering solutions to common challenges in data handling, model training, evaluation, and deployment.</p><p>You'll learn to clean, augment, and annotate large-scale datasets, architect modular training pipelines, and optimize models using hyperparameter tuning, pruning, and quantization. The chapters help you explore regularization, checkpointing, fine-tuning, and advanced prompting methods, such as reason-and-act, as well as implement reflection, multi-step reasoning, and tool use for intelligent task completion. The book also highlights Retrieval-Augmented Generation (RAG), graph-based retrieval, interpretability, fairness, and RLHF, culminating in the creation of agentic LLM systems.</p><p>By the end of this book, you'll be equipped with the knowledge and tools to build next-generation LLMs that are adaptable, efficient, safe, and aligned with human values.</p><p>What You Will Learn: </p><p>- Implement efficient data prep techniques, including cleaning and augmentation</p><p>-