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Home > Data > GPT/LLMs > LLM Design Patterns
LLM Design Patterns
LLM Design Patterns

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

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LLM Design Patterns

Introduction to LLM Design Patterns

Large language models (LLMs) are machine learning models capable of understanding and producing human-like text across diverse domains. They have opened up unprecedented possibilities while also presenting unique challenges.

In this chapter, we will introduce the world of LLMs and the critical role of design patterns in their development. You will learn about the evolution of language models, explore the core principles that power modern LLMs, and examine their impressive capabilities, as well as their limitations. We’ll uncover the importance of design patterns – time-tested solutions to recurring problems in software development – and how they are being adapted and applied to address the specific challenges of LLM projects.

In this chapter, we’ll be covering the following topics:

  • Understanding LLMs
  • Understanding design patterns
  • Design patterns for LLM development
  • Future directions in LLM patterns...

Understanding LLMs

In this section, we will highlight the core concepts of LLMs, exploring their evolution, underlying principles, and the transformative impact they have had on the AI landscape. We will examine the key components that make LLMs so powerful, the challenges they present, and the ongoing developments shaping their future.

The evolution of language models

The journey toward modern LLMs has been marked by significant paradigm shifts in natural language processing, as illustrated in the timeline shown in Figure 1.1:

Figure 1.1 – Evolution of language models

Figure 1.1 – Evolution of language models

Early statistical approaches, while groundbreaking, were limited in capturing the nuances of human language. The advent of neural networks, particularly recurrent neural networks (RNNs) and long short-term memory (LSTM) networks, allowed for better handling of sequential data and improved the ability to capture longer-term dependencies in text. Capturing longer-term dependencies...

Understanding design patterns

Design patterns originated as a way to capture and share solutions to recurring design problems. Initially rooted in object-oriented programming, they offered a structured approach to building software by identifying repeatable strategies that enhance code clarity, reusability, and maintainability. Over time, design patterns have evolved beyond their original context, influencing a wide range of development practices and system architectures, including LLM development. The following discussion traces the origins of design patterns and outlines the principles that have shaped their continued relevance across different programming paradigms and application domains.

Origins and evolution

The concept of design patterns in software engineering gained prominence in the 1990s, largely popularized by the book Design Patterns: Elements of Reusable Object-Oriented Software by Erich Gamma, Richard Helm, Ralph Johnson, and John Vlissides, often referred to as...

Design patterns for LLM development

As the need to develop intelligent LLM-based applications grows, we see the emergence of specific design patterns tailored to address the unique challenges posed by these complex systems. These patterns differ significantly from traditional software design patterns, focusing on aspects inherent to the entire life cycle of LLMs – from data preparation and model training to evaluation, deployment, and sophisticated application design.

This book delves into 29 practical LLM design patterns, explored in detail across Chapters 2 through 30. Developers and researchers can navigate the complexities of building LLM systems using these design patterns:

  • Establishing a solid data foundation (Chapters 2–6): Lay the groundwork for high-quality models by mastering patterns for data cleaning (Chapter 2), data augmentation (Chapter 3), handling large datasets (Chapter 4), implementing data versioning (Chapter 5), and ensuring effective dataset...

Summary

This chapter provided a foundational understanding of LLMs and introduced the role of design patterns in their development. It traced the evolution of language models from early statistical approaches to the transformer architecture-based LLMs of today, emphasizing key features such as the self-attention mechanism, the significance of scale and computational resources, few-shot learning, language understanding and generation capabilities, and multilingual abilities.

Then, this chapter transitioned to the importance of design patterns, drawing parallels with their established role in software engineering. This highlighted the benefits of applying design patterns to LLM development, outlining a structured approach for improving data quality, optimizing training, addressing model quality and alignment, enhancing reasoning capabilities, integrating external knowledge through RAG, and developing agentic applications. Then, the 29 patterns that will be explored throughout this...

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Key benefits

  • 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

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.

Who is this book 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.

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
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Publication date : May 30, 2025
Length: 534 pages
Edition : 1st
Language : English
ISBN-13 : 9781836207030
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Publication date : May 30, 2025
Length: 534 pages
Edition : 1st
Language : English
ISBN-13 : 9781836207030
Category :
Data
Concepts :
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Table of Contents

37 Chapters
Part 1: Introduction and Data Preparation Chevron down icon Chevron up icon
Part 1: Introduction and Data Preparation
Chapter 1: Introduction to LLM Design Patterns Chevron down icon Chevron up icon
Chapter 1: Introduction to LLM Design Patterns
Understanding LLMs
Understanding design patterns
Design patterns for LLM development
Summary
Chapter 2: Data Cleaning for LLM Training Chevron down icon Chevron up icon
Chapter 2: Data Cleaning for LLM Training
Understanding the importance of clean data
Common data quality issues in language datasets
Text preprocessing techniques for LLMs
Handling multilingual and code-mixed data
Deduplication strategies for large text corpora
Automated data cleaning pipelines
Data validation and quality assurance
Summary
Chapter 3: Data Augmentation Chevron down icon Chevron up icon
Chapter 3: Data Augmentation
Text data augmentation techniques
Leveraging existing LLMs for data generation
Multilingual data augmentation strategies
Semantic preservation in text augmentation
Balancing augmentation and data quality
Evaluating the impact of data augmentation
Summary
Chapter 4: Handling Large Datasets for LLM Training Chevron down icon Chevron up icon
Chapter 4: Handling Large Datasets for LLM Training
Challenges of large datasets
Data sampling techniques
Distributed data processing
Data sharding and parallelization strategies
Efficient data storage formats
Streaming data processing for continuous LLM training
Memory-efficient data loading techniques
Summary
Chapter 5: Data Versioning Chevron down icon Chevron up icon
Chapter 5: Data Versioning
Understanding the need for data versioning
Data versioning strategies for large language datasets
Tools for data versioning
Integrating data versioning in training workflows
Version control for text corpora
Managing dataset variants and experiments
Best practices for data versioning
Summary
Chapter 6: Dataset Annotation and Labeling Chevron down icon Chevron up icon
Chapter 6: Dataset Annotation and Labeling
The importance of quality annotations
Annotation strategies for different tasks
Tools and platforms for large-scale text annotation
Managing annotation quality
Crowdsourcing annotations – benefits and challenges
Semi-automated annotation techniques
Scaling annotation processes for massive language datasets
Annotation biases and mitigation strategies
Summary
Part 2: Training and Optimization of Large Language Models Chevron down icon Chevron up icon
Part 2: Training and Optimization of Large Language Models
Chapter 7: Training Pipeline Chevron down icon Chevron up icon
Chapter 7: Training Pipeline
Components of a training pipeline
Data input and preprocessing
LLM architecture design considerations
Loss functions and optimization strategies
Logging
Pipeline modularity and reusability
Scaling your training pipeline for larger models
Summary
Chapter 8: Hyperparameter Tuning Chevron down icon Chevron up icon
Chapter 8: Hyperparameter Tuning
Understanding hyperparameters
Manual versus automated tuning
Grid and random search
Bayesian optimization
Population-based methods
Multi-objective hyperparameter optimization
Hyperparameter tuning at scale – challenges and solutions
Summary
Chapter 9: Regularization Chevron down icon Chevron up icon
Chapter 9: Regularization
L2 regularization (Ridge regression)
Dropout
Layer-wise adaptive regularization
Gradient clipping and noise injection
Regularization in transfer learning and fine-tuning scenarios
Emerging regularization techniques
Summary
Chapter 10: Checkpointing and Recovery Chevron down icon Chevron up icon
Chapter 10: Checkpointing and Recovery
Why is checkpointing important?
Checkpoint frequency and storage strategies
Efficient checkpoint formats
Recovering from failures
Checkpointing in distributed LLM training
Version control for LLM checkpoints
Automated checkpointing and recovery systems
Summary
Chapter 11: Fine-Tuning Chevron down icon Chevron up icon
Chapter 11: Fine-Tuning
Implementing transfer learning and fine-tuning
Strategies for freezing and unfreezing layers
Learning rate scheduling
Domain-specific fine-tuning techniques
Few-shot and zero-shot fine-tuning
Continual fine-tuning and catastrophic forgetting
Summary
Chapter 12: Model Pruning Chevron down icon Chevron up icon
Chapter 12: Model Pruning
Magnitude-based pruning
Structured versus unstructured pruning
Iterative pruning techniques
Pruning during training versus post-training pruning
Balancing pruning and model performance
Combining pruning with other compression techniques
Summary
Chapter 13: Quantization Chevron down icon Chevron up icon
Chapter 13: Quantization
Understanding the basics
Mixed-precision quantization
Hardware-specific considerations
Comparing quantization strategies
Combining quantization with other optimization techniques
Summary
Part 3: Evaluation and Interpretation of Large Language Models Chevron down icon Chevron up icon
Part 3: Evaluation and Interpretation of Large Language Models
Chapter 14: Evaluation Metrics Chevron down icon Chevron up icon
Chapter 14: Evaluation Metrics
NLU benchmarks
Reasoning and problem-solving metrics
Coding and programming evaluation
Conversational ability assessment
Commonsense and general knowledge benchmarks
Other commonly used benchmarks
Developing custom metrics and benchmarks
Interpreting and comparing LLM evaluation results
Summary
Chapter 15: Cross-Validation Chevron down icon Chevron up icon
Chapter 15: Cross-Validation
Pre-training and fine-tuning data splits
Few-shot and zero-shot evaluation strategies
Domain and task generalization
Continual learning evaluation
Cross-validation challenges and best practices
Summary
Chapter 16: Interpretability Chevron down icon Chevron up icon
Chapter 16: Interpretability
Attention visualization techniques
Probing methods
Explaining LLM predictions with attribution methods
Interpretability in transformer-based LLMs
Mechanistic interpretability
Trade-offs between interpretability and performance
Summary
Chapter 17: Fairness and Bias Detection Chevron down icon Chevron up icon
Chapter 17: Fairness and Bias Detection
Types of bias
Fairness metrics for LLM text generation and understanding
Detecting bias
Debiasing strategies
Fairness-aware training
Ethical considerations
Summary
Chapter 18: Adversarial Robustness Chevron down icon Chevron up icon
Chapter 18: Adversarial Robustness
Types of textual adversarial attacks
Adversarial training techniques
Evaluating robustness
Trade-offs in the adversarial training of LLMs
Real-world implications
Summary
Chapter 19: Reinforcement Learning from Human Feedback Chevron down icon Chevron up icon
Chapter 19: Reinforcement Learning from Human Feedback
Components of RLHF systems
Scaling RLHF
Limitations of RLHF in language modeling
Applications of RLHF
Summary
Part 4: Advanced Prompt Engineering Techniques Chevron down icon Chevron up icon
Part 4: Advanced Prompt Engineering Techniques
Chapter 20: Chain-of-Thought Prompting Chevron down icon Chevron up icon
Chapter 20: Chain-of-Thought Prompting
Designing effective CoT prompts
Using CoT prompting for problem solving
Combining CoT prompting with other techniques
Evaluating CoT prompting outputs
Limitations of CoT prompting
Future directions
Summary
Chapter 21: Tree-of-Thoughts Prompting Chevron down icon Chevron up icon
Chapter 21: Tree-of-Thoughts Prompting
Designing ToT prompts
Search strategies
Pruning and evaluation
Applying ToT to solve a multi-step problem
Challenges in implementation
Future directions
Summary
Chapter 22: Reasoning and Acting Chevron down icon Chevron up icon
Chapter 22: Reasoning and Acting
Implementing ReAct in LangChain
Building ReAct agents with LangChain’s Expression Language
Completing tasks and solving problems
Evaluating ReAct’s performance
Safety, control, and ethical considerations
Limitations and future directions
Summary
Chapter 23: Reasoning WithOut Observation Chevron down icon Chevron up icon
Chapter 23: Reasoning WithOut Observation
Implementing ReWOO with LangGraph
Advantages of ReWOO
Evaluating quality and ethical considerations
Future directions
Summary
Chapter 24: Reflection Techniques Chevron down icon Chevron up icon
Chapter 24: Reflection Techniques
Designing prompts for self-reflection
Implementing iterative refinement
Correcting errors
Evaluating the impact of reflection
Challenges in implementing effective reflection
Future directions
Summary
Chapter 25: Automatic Multi-Step Reasoning and Tool Use Chevron down icon Chevron up icon
Chapter 25: Automatic Multi-Step Reasoning and Tool Use
Designing prompts for complex task decomposition
Integrating external tools
Implementing automatic tool selection and use
Complex problem solving
Evaluating multi-step reasoning and tool use
Challenges and future directions
Summary
Part 5: Retrieval and Knowledge Integration in Large Language Models Chevron down icon Chevron up icon
Part 5: Retrieval and Knowledge Integration in Large Language Models
Chapter 26: Retrieval-Augmented Generation Chevron down icon Chevron up icon
Chapter 26: Retrieval-Augmented Generation
Building a simple RAG system for LLMs
Embeddings and indexing for retrieval in LLM applications
Query formulation strategies in LLM-based RAG
Integrating retrieved information with LLM generation
Challenges and opportunities in RAG for LLMs
Summary
Chapter 27: Graph-Based RAG Chevron down icon Chevron up icon
Chapter 27: Graph-Based RAG
Introduction to graph-based knowledge representation for LLMs
Designing graph RAG architectures for LLMs
Graph embedding techniques for LLM retrieval
Query expansion using graph structures in LLMs
Applications and use cases of graph RAG in LLMs
Challenges and future directions in graph-based RAG
Summary
Chapter 28: Advanced RAG Chevron down icon Chevron up icon
Chapter 28: Advanced RAG
Multi-step and iterative retrieval techniques for LLMs
Adaptive retrieval based on context and task in LLMs
Meta-learning for improved retrieval in LLMs
Combining RAG with other LLM prompting techniques
Handling ambiguity and uncertainty in LLM-based RAG
Scaling RAG to very large knowledge bases
Future directions in RAG research for LLMs
Summary
Chapter 29: Evaluating RAG Systems Chevron down icon Chevron up icon
Chapter 29: Evaluating RAG Systems
Challenges in evaluating RAG systems for LLMs
Metrics for assessing retrieval quality in LLM-based RAG
Considerations for retrieval metrics in RAG
Evaluating the relevance of retrieved information for LLMs
Measuring the impact of retrieval on LLM generation
End-to-end evaluation of RAG systems in LLMs
Human evaluation techniques for LLM-based RAG
Benchmarks and datasets for RAG evaluation
Summary
Chapter 30: Agentic Patterns Chevron down icon Chevron up icon
Chapter 30: Agentic Patterns
Introduction to agentic AI systems based on LLMs
Goal-setting and planning in LLM-based agents
Implementing memory and state management for LLM agents
Decision-making and action selection in LLM-based agents
Learning and adaptation in agentic LLM systems
Ethical considerations and safety in LLM-based agentic AI
Future prospects of agentic AI using LLMs
Summary
Future directions in LLM patterns and their development
Index Chevron down icon Chevron up icon
Index
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About the author

Profile icon Ken Huang
Ken Huang
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Ken Huang is a renowned AI expert, serving as co-chair of AI Safety Working Groups at Cloud Security Alliance and the AI STR Working Group at World Digital Technology Academy under the UN Framework. As CEO of DistributedApps, he provides specialized GenAI consulting. A key contributor to OWASP's Top 10 Risks for LLM Applications and NIST's Generative AI Working Group, Huang has authored influential books including Beyond AI (Springer, 2023), Generative AI Security (Springer, 2024), and Agentic AI: Theories and Practice (Springer, 2025) He's a global speaker at prestigious events such as Davos WEF, ACM, IEEE, and RSAC. Huang is also a member of the OpenAI Forum and project leader for the OWASP AI Vulnerability Scoring System project.
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  16. Ukraine
  17. Venezuela
What is custom duty/charge? Chevron down icon Chevron up icon

Customs duty are charges levied on goods when they cross international borders. It is a tax that is imposed on imported goods. These duties are charged by special authorities and bodies created by local governments and are meant to protect local industries, economies, and businesses.

Do I have to pay customs charges for the print book order? Chevron down icon Chevron up icon

The orders shipped to the countries that are listed under EU27 will not bear custom charges. They are paid by Packt as part of the order.

List of EU27 countries: www.gov.uk/eu-eea:

A custom duty or localized taxes may be applicable on the shipment and would be charged by the recipient country outside of the EU27 which should be paid by the customer and these duties are not included in the shipping charges been charged on the order.

How do I know my custom duty charges? Chevron down icon Chevron up icon

The amount of duty payable varies greatly depending on the imported goods, the country of origin and several other factors like the total invoice amount or dimensions like weight, and other such criteria applicable in your country.

For example:

  • If you live in Mexico, and the declared value of your ordered items is over $ 50, for you to receive a package, you will have to pay additional import tax of 19% which will be $ 9.50 to the courier service.
  • Whereas if you live in Turkey, and the declared value of your ordered items is over € 22, for you to receive a package, you will have to pay additional import tax of 18% which will be € 3.96 to the courier service.
How can I cancel my order? Chevron down icon Chevron up icon

Cancellation Policy for Published Printed Books:

You can cancel any order within 1 hour of placing the order. Simply contact customercare@packt.com with your order details or payment transaction id. If your order has already started the shipment process, we will do our best to stop it. However, if it is already on the way to you then when you receive it, you can contact us at customercare@packt.com using the returns and refund process.

Please understand that Packt Publishing cannot provide refunds or cancel any order except for the cases described in our Return Policy (i.e. Packt Publishing agrees to replace your printed book because it arrives damaged or material defect in book), Packt Publishing will not accept returns.

What is your returns and refunds policy? Chevron down icon Chevron up icon

Return Policy:

We want you to be happy with your purchase from Packtpub.com. We will not hassle you with returning print books to us. If the print book you receive from us is incorrect, damaged, doesn't work or is unacceptably late, please contact Customer Relations Team on customercare@packt.com with the order number and issue details as explained below:

  1. If you ordered (eBook, Video or Print Book) incorrectly or accidentally, please contact Customer Relations Team on customercare@packt.com within one hour of placing the order and we will replace/refund you the item cost.
  2. Sadly, if your eBook or Video file is faulty or a fault occurs during the eBook or Video being made available to you, i.e. during download then you should contact Customer Relations Team within 14 days of purchase on customercare@packt.com who will be able to resolve this issue for you.
  3. You will have a choice of replacement or refund of the problem items.(damaged, defective or incorrect)
  4. Once Customer Care Team confirms that you will be refunded, you should receive the refund within 10 to 12 working days.
  5. If you are only requesting a refund of one book from a multiple order, then we will refund you the appropriate single item.
  6. Where the items were shipped under a free shipping offer, there will be no shipping costs to refund.

On the off chance your printed book arrives damaged, with book material defect, contact our Customer Relation Team on customercare@packt.com within 14 days of receipt of the book with appropriate evidence of damage and we will work with you to secure a replacement copy, if necessary. Please note that each printed book you order from us is individually made by Packt's professional book-printing partner which is on a print-on-demand basis.

What tax is charged? Chevron down icon Chevron up icon

Currently, no tax is charged on the purchase of any print book (subject to change based on the laws and regulations). A localized VAT fee is charged only to our European and UK customers on eBooks, Video and subscriptions that they buy. GST is charged to Indian customers for eBooks and video purchases.

What payment methods can I use? Chevron down icon Chevron up icon

You can pay with the following card types:

  1. Visa Debit
  2. Visa Credit
  3. MasterCard
  4. PayPal
What is the delivery time and cost of print books? Chevron down icon Chevron up icon

Shipping Details

USA:

'

Economy: Delivery to most addresses in the US within 10-15 business days

Premium: Trackable Delivery to most addresses in the US within 3-8 business days

UK:

Economy: Delivery to most addresses in the U.K. within 7-9 business days.
Shipments are not trackable

Premium: Trackable delivery to most addresses in the U.K. within 3-4 business days!
Add one extra business day for deliveries to Northern Ireland and Scottish Highlands and islands

EU:

Premium: Trackable delivery to most EU destinations within 4-9 business days.

Australia:

Economy: Can deliver to P. O. Boxes and private residences.
Trackable service with delivery to addresses in Australia only.
Delivery time ranges from 7-9 business days for VIC and 8-10 business days for Interstate metro
Delivery time is up to 15 business days for remote areas of WA, NT & QLD.

Premium: Delivery to addresses in Australia only
Trackable delivery to most P. O. Boxes and private residences in Australia within 4-5 days based on the distance to a destination following dispatch.

India:

Premium: Delivery to most Indian addresses within 5-6 business days

Rest of the World:

Premium: Countries in the American continent: Trackable delivery to most countries within 4-7 business days

Asia:

Premium: Delivery to most Asian addresses within 5-9 business days

Disclaimer:
All orders received before 5 PM U.K time would start printing from the next business day. So the estimated delivery times start from the next day as well. Orders received after 5 PM U.K time (in our internal systems) on a business day or anytime on the weekend will begin printing the second to next business day. For example, an order placed at 11 AM today will begin printing tomorrow, whereas an order placed at 9 PM tonight will begin printing the day after tomorrow.


Unfortunately, due to several restrictions, we are unable to ship to the following countries:

  1. Afghanistan
  2. American Samoa
  3. Belarus
  4. Brunei Darussalam
  5. Central African Republic
  6. The Democratic Republic of Congo
  7. Eritrea
  8. Guinea-bissau
  9. Iran
  10. Lebanon
  11. Libiya Arab Jamahriya
  12. Somalia
  13. Sudan
  14. Russian Federation
  15. Syrian Arab Republic
  16. Ukraine
  17. Venezuela