LLM Design Patterns

<p><strong>Explore reusable design patterns including data-centric approaches model development model fine-tuning and RAG for LLM application development and advanced prompting techniques</strong></p><p><strong>Key Features:</strong></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><strong>Book Description:</strong></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><strong>What You Will Learn:</strong></p><p>- Implement efficient data prep techniques including cleaning and augmentation</p><p>- Design scalable training pipelines with tuning regularization and checkpointing</p><p>- Optimize LLMs via pruning quantization and fine-tuning</p><p>- Evaluate models with metrics cross-validation and interpretability</p><p>- Understand fairness and detect bias in outputs</p><p>- Develop RLHF strategies to build secure agentic AI systems</p><p><strong>Who this book is for:</strong></p><p>This 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.</p><p><strong>Table of Contents</strong></p><p>- Introduction to LLM Design Patterns</p><p>- Data Cleaning for LLM Training</p><p>- Data Augmentation</p><p>- Handling Large Datasets for LLM Training</p><p>- Data Versioning</p><p>- Dataset Annotation and Labeling</p><p>- Training Pipeline</p><p>- Hyperparameter Tuning</p><p>- Regularization</p><p>- Checkpointing and Recovery</p><p>- Fine-Tuning</p><p>- Model Pruning</p><p>- Quantization</p><p>- Evaluation Metrics</p><p>- Cross-Validation</p><p>- Interpretability</p><p>- Fairness and Bias Detection</p><p>- Adversarial Robustness</p><p>- Reinforcement Learning from Human Feedback</p><p>- Chain-of-Thought Prompting</p><p>- Tree-of-Thoughts Prompting</p><p>- Reasoning and Acting</p><p>- Reasoning WithOut Observation</p><p>- Reflection Techniques</p><p>- Automatic Multi-Step Reasoning and Tool Use</p><p>- Retrieval-Augmented Generation</p><p>- Graph-Based RAG</p><p>- Advanced RAG</p><p>- Evaluating RAG Systems</p><p>- Agentic Patterns</p>