LLM Design Patterns
Packt Publishing Limited (Verlag)
978-1-83620-703-0 (ISBN)
Free with your book: PDF Copy, AI Assistant, and Next-Gen Reader
Key Features
Learn comprehensive LLM development, including data prep, training pipelines, and optimization
Explore advanced prompting techniques, such as chain-of-thought, tree-of-thought, RAG, and AI agents
Implement evaluation metrics, interpretability, and bias detection for fair, reliable models
Book 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 learn
Implement efficient data prep techniques, including cleaning and augmentation
Design scalable training pipelines with tuning, regularization, and checkpointing
Optimize LLMs via pruning, quantization, and fine-tuning
Evaluate models with metrics, cross-validation, and interpretability
Understand fairness and detect bias in outputs
Develop RLHF strategies to build secure, agentic AI systems
Who 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.
Ken Huang is an acclaimed author of 8 books on AI and Web3. He is the Co-Chair of the AI Organizational Responsibility Working Group and AI Control Framework at the Cloud Security Alliance. In addition, Huang contributed extensively to key initiatives in the space. He is a core contributor to OWASP's Top 10 Risks for LLM Applications and heavily involved in the NIST Generative AI Public Working Group. He also provides feedback on publications like NIST SP 800-226. A sought-after speaker, Ken has shared his insights at renowned global conferences, including those hosted by Davos WEF, ACM, IEEE, and CSA AI Summit, CSA AI Think Tank Day and World Bank. His recent co-authorship of "Blockchain and Web3: Building the Cryptocurrency, Privacy, and Security Foundations of the Metaverse" adds to his reputation, with the book being recognized as one of the must-reads in both 2023 and 2024 by TechTarget.
Table of Contents
Introduction to LLM Design Patterns
Data Cleaning for LLM Training
Data Augmentation
Handling Large Datasets for LLM Training
Data Versioning
Dataset Annotation and Labeling
Training Pipeline
Hyperparameter Tuning
Regularization
Checkpointing and Recovery
Fine-Tuning
Model Pruning
Quantization
Evaluation Metrics
Cross-Validation
Interpretability
Fairness and Bias Detection
Adversarial Robustness
Reinforcement Learning from Human Feedback
Chain-of-Thought Prompting
Tree-of-Thoughts Prompting
Reasoning and Acting
Reasoning WithOut Observation
Reflection Techniques
Automatic Multi-Step Reasoning and Tool Use
Retrieval-Augmented Generation
Graph-Based RAG
Advanced RAG
Evaluating RAG Systems
Agentic Patterns
| Erscheinungsdatum | 21.05.2025 |
|---|---|
| Verlagsort | Birmingham |
| Sprache | englisch |
| Maße | 191 x 235 mm |
| Themenwelt | Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik |
| ISBN-10 | 1-83620-703-4 / 1836207034 |
| ISBN-13 | 978-1-83620-703-0 / 9781836207030 |
| Zustand | Neuware |
| Informationen gemäß Produktsicherheitsverordnung (GPSR) | |
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