LLM Design Patterns

Explore reusable design patterns, including data-centric approaches, model development, model fine-tuning, and RAG for LLM application development and advanced prompting techniquesKey FeaturesLearn comprehensive LLM development, including data prep, training pipelines, and optimizationExplore advanced prompting techniques, such as chain-of-thought, tree-of-thought, RAG, and AI agentsImplement evaluation metrics, interpretability, and bias detection for fair, reliable modelsPrint or Kindle purchase includes a free PDF eBookBook DescriptionThis 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. 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. 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. What you will learnImplement efficient data prep techniques, including cleaning and augmentationDesign scalable training pipelines with tuning, regularization, and checkpointingOptimize LLMs via pruning, quantization, and fine-tuningEvaluate models with metrics, cross-validation, and interpretabilityUnderstand fairness and detect bias in outputsDevelop RLHF strategies to build secure, agentic AI systemsWho this book is forThis book is essential for AI engineers, architects, data scientists, and software engineers responsible for developing and deploying AI systems powered by large language models. A basic understanding of machine learning concepts and experience in Python programming is a must.]]>