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

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
Print or Kindle purchase includes a free PDF eBook
Book Description
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.

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 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.

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