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 techniquesFree with your book: PDF Copy, AI Assistant, and Next-Gen ReaderKey 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 modelsBook 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.Table of ContentsIntroduction to LLM Design PatternsData Cleaning for LLM TrainingData AugmentationHandling Large Datasets for LLM TrainingData VersioningDataset Annotation and LabelingTraining PipelineHyperparameter TuningRegularizationCheckpointing and RecoveryFine-TuningModel PruningQuantizationEvaluation MetricsCross-ValidationInterpretabilityFairness and Bias DetectionAdversarial RobustnessReinforcement Learning from Human FeedbackChain-of-Thought PromptingTree-of-Thoughts PromptingReasoning and ActingReasoning WithOut ObservationReflection TechniquesAutomatic Multi-Step Reasoning and Tool UseRetrieval-Augmented Generation Graph-Based RAGAdvanced RAGEvaluating RAG SystemsAgentic Patterns Read more