What this book covers

Chapter 1, Introduction to LLM Design Patterns, provides a foundational understanding of LLMs and introduces the critical role of design patterns in their development.

Chapter 2, Data Cleaning for LLM Training, equips you with practical tools and techniques that allow you to effectively clean your data for LLM training.

Chapter 3, Data Augmentation, helps you understand the data augmentation pattern in depth, from increasing the diversity of your training dataset to maintaining its integrity.

Chapter 4, Handling Large Datasets for LLM Training, allows you to learn advanced techniques for managing and processing massive datasets essential for training state-of-the-art LLMs.

Chapter 5, Data Versioning, shows you how to implement effective data versioning strategies for LLM development.

Chapter 6, Dataset Annotation and Labeling, lets you explore advanced techniques for creating well-annotated datasets that can significantly impact your LLM’s performance across various tasks.

Chapter 7, Training Pipeline, helps you understand the key components of an LLM training pipeline, from data ingestion and preprocessing to model architecture and optimization strategies.

Chapter 8, Hyperparameter Tuning, demonstrates what the hyperparameters in LLMs are and strategies for optimizing them efficiently.

Chapter 9, Regularization, shows you different regularization techniques that are specifically tailored to LLMs.

Chapter 10, Checkpointing and Recovery, outlines strategies for determining optimal checkpoint frequency, efficient storage formats for large models, and techniques for recovering from various types of failures.

Chapter 11, Fine-Tuning, teaches you effective strategies for fine-tuning pre-trained language models.

Chapter 12, Model Pruning, lets you explore model pruning techniques, designed to reduce model size while maintaining performance.

Chapter 13, Quantization, gives you a look into quantization methods that can optimize LLMs for deployment on resource-constrained devices.

Chapter 14, Evaluation Metrics, explores the most recent and commonly used benchmarks for evaluating LLMs across various domains.

Chapter 15, Cross-Validation, shows you how to explore cross-validation strategies specifically designed for LLMs.

Chapter 16, Interpretability, helps you understand how interpretability in LLMs refers to the model’s ability to understand and explain how the model processes inputs and generates outputs.

Chapter 17, Fairness and Bias Detection, demonstrates that fairness in LLMs involves ensuring that the model’s outputs and decisions do not discriminate against or unfairly treat individuals or groups based on protected attributes.

Chapter 18, Adversarial Robustness, helps you understand that adversarial attacks on LLMs are designed to manipulate the model’s output by making small, often imperceptible changes to the input.

Chapter 19, Reinforcement Learning from Human Feedback, takes you through a powerful technique for aligning LLMs with human preferences.

Chapter 20, Chain-of-Thought Prompting, demonstrates how you can leverage chain-of-thought prompting to improve your LLM’s performance on complex reasoning tasks.

Chapter 21, Tree-of-Thoughts Prompting, allows you to implement tree-of-thoughts prompting to tackle complex reasoning tasks with your LLMs.

Chapter 22, Reasoning and Acting, teaches you about the ReAct framework, a powerful technique for prompting your LLMs to not only reason through complex scenarios but also plan and simulate the execution of actions, similar to how humans operate in the real world.

Chapter 23, Reasoning WithOut Observation, teaches you the framework for providing LLMs with the ability to reason about hypothetical situations and leverage external tools effectively.

Chapter 24, Reflection Techniques, demonstrates reflection in LLMs, which refers to a model’s ability to analyze, evaluate, and improve its own outputs.

Chapter 25, Automatic Multi-Step Reasoning and Tool Use, helps you understand how automatic multi-step reasoning and tool use significantly expand the problem-solving capabilities of LLMs, enabling them to tackle complex, real-world tasks.

Chapter 26, Retrieval-Augmented Generation, takes you through a technique that enhances the performance of Al models, particularly in tasks that require knowledge or data not contained within the model’s pre-trained parameters.

Chapter 27, Graph-Based RAG, shows how to leverage graph-structured knowledge in RAG for LLMs.

Chapter 28, Advanced RAG, demonstrates how you can move beyond these basic RAG methods and explore more sophisticated techniques designed to enhance LLM performance across a wide range of tasks.

Chapter 29, Evaluating RAG Systems, equips you with the knowledge necessary to assess the ability of RAG systems to produce accurate, relevant, and factually grounded responses.

Chapter 30, Agentic Patterns, shows you how agentic Al systems using LLMs can be designed to operate autonomously, make decisions, and take actions to achieve specified goals.