You're reading from LLM Design Patterns A Practical Guide to Building Robust and Efficient AI Systems

Product type Paperback

Published in May 2025

Publisher Packt

ISBN-13 9781836207030

Length 534 pages

Edition 1st Edition

Concepts

GPT/LLMs

Author (1):

Ken Huang

View More author details

Table of Contents (38) Chapters

Preface

1. Part 1: Introduction and Data Preparation

2. Chapter 1: Introduction to LLM Design Patterns FREE CHAPTER

3. Chapter 2: Data Cleaning for LLM Training

4. Chapter 3: Data Augmentation

5. Chapter 4: Handling Large Datasets for LLM Training

6. Chapter 5: Data Versioning

7. Chapter 6: Dataset Annotation and Labeling

8. Part 2: Training and Optimization of Large Language Models

9. Chapter 7: Training Pipeline

10. Chapter 8: Hyperparameter Tuning

11. Chapter 9: Regularization

12. Chapter 10: Checkpointing and Recovery

13. Chapter 11: Fine-Tuning

14. Chapter 12: Model Pruning

15. Chapter 13: Quantization

16. Part 3: Evaluation and Interpretation of Large Language Models

17. Chapter 14: Evaluation Metrics

18. Chapter 15: Cross-Validation

19. Chapter 16: Interpretability

20. Chapter 17: Fairness and Bias Detection

21. Chapter 18: Adversarial Robustness

22. Chapter 19: Reinforcement Learning from Human Feedback

23. Part 4: Advanced Prompt Engineering Techniques

24. Chapter 20: Chain-of-Thought Prompting

25. Chapter 21: Tree-of-Thoughts Prompting

26. Chapter 22: Reasoning and Acting

27. Chapter 23: Reasoning WithOut Observation

28. Chapter 24: Reflection Techniques

29. Chapter 25: Automatic Multi-Step Reasoning and Tool Use

30. Part 5: Retrieval and Knowledge Integration in Large Language Models

31. Chapter 26: Retrieval-Augmented Generation

32. Chapter 27: Graph-Based RAG

33. Chapter 28: Advanced RAG

34. Chapter 29: Evaluating RAG Systems

35. Chapter 30: Agentic Patterns

36. Index

Why subscribe?

37. Other Books You May Enjoy

To get the most out of this book

To get the most out of this book, you should ideally have a foundational understanding of machine learning concepts and basic proficiency in Python programming. These prerequisites will help in grasping the technical methodologies and implementation strategies discussed throughout the chapters. Machine learning knowledge is essential for understanding key aspects of LLM development, such as model training, hyperparameter tuning, regularization techniques, and optimization processes. Python programming skills are particularly valuable as they enable you to implement and experiment with the design patterns, workflows, and algorithms presented in the book.

Familiarity with natural language processing (NLP) frameworks and tools, such as Hugging Face Transformers, spaCy, or NLTK, will further enhance the learning experience. These frameworks are commonly used in LLM development and provide a practical means to work with pre-trained models, tokenize text, and process linguistic data. Understanding how these tools function will enable you to focus on the higher-level concepts and design patterns without being bogged down by foundational programming or NLP operations.

For those less familiar with these areas, supplementary resources on machine learning basics, Python programming, and NLP tools are recommended. This book’s approach ensures that with some effort to bridge knowledge gaps, you can successfully navigate its concepts and apply them effectively in real-world projects.

Note

This book provides code snippets to illustrate LLM design patterns and implementation concepts. The code is intentionally focused on demonstrating ideas in a concise and readable way, rather than offering complete, executable programs. It is not intended for direct deployment or integration into production environments. You are encouraged to study and adapt the code to your own context, rather than copying and pasting it as is. For this reason, there is no accompanying GitHub repository; the examples presented are self-contained within the book and sufficient for understanding the intended concepts without requiring external code bases.