LLM Design Patterns: A Practical Guide to Building Robust and Efficient AI Systems

Ken Huang

Explore reusable design patterns, including data-centric approaches, model development, model fine-tuning, and RAG for LLM application development and advanced prompting techniques

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

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 for
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.