Unleash the transformative potential of GenAI with this comprehensive guide that serves as an indispensable roadmap for integrating large language models into real-world applications. Gain invaluable insights into identifying compelling use cases, leveraging state-of-the-art models effectively, deploying these models into your applications at scale, and navigating ethical considerations.
Key Features
- Get familiar with the most important tools and concepts used in real scenarios to design GenAI apps
- Interact with GenAI models to tailor model behavior to minimize hallucinations
- Get acquainted with a variety of strategies and an easy to follow 4 step frameworks for integrating GenAI into applications
Book Description
Explore the transformative potential of GenAI in the application development lifecycle. Through concrete examples, you will go through the process of ideation and integration, understanding the tradeoffs and the decision points when integrating GenAI.
With recent advances in models like Google Gemini, Anthropic Claude, DALL-E and GPT-4o, this timely resource will help you harness these technologies through proven design patterns.
We then delve into the practical applications of GenAI, identifying common use cases and applying design patterns to address real-world challenges. From summarization and metadata extraction to intent classification and question answering, each chapter offers practical examples and blueprints for leveraging GenAI across diverse domains and tasks. You will learn how to fine-tune models for specific applications, progressing from basic prompting to sophisticated strategies such as retrieval augmented generation (RAG) and chain of thought.
Additionally, we provide end-to-end guidance on operationalizing models, including data prep, training, deployment, and monitoring. We also focus on responsible and ethical development techniques for transparency, auditing, and governance as crucial design patterns.
What you will learn
- Concepts of GenAI: pre-training, fine-tuning, prompt engineering, and RAG
- Framework for integrating AI: entry points, prompt pre-processing, inference, post-processing, and presentation
- Patterns for batch and real-time integration
- Code samples for metadata extraction, summarization, intent classification, question-answering with RAG, and more
- Ethical use: bias mitigation, data privacy, and monitoring
- Deployment and hosting options for GenAI models
Who this book is for
This book is not an introduction to AI/ML or Python. It offers practical guides for designing, building, and deploying GenAI applications in production. While all readers are welcome, those who benefit most include:
Developer engineers with foundational tech knowledge
Software architects seeking best practices and design patterns
Professionals using ML for data science, research, etc., who want a deeper understanding of Generative AI
Technical product managers with a software development background
This concise focus ensures practical, actionable insights for experienced professionals
Table of Contents
- Introduction to Generative AI Design Patterns
- Identifying Generative AI Use Cases
- Designing Patterns for Interacting with Generative AI
- Generative AI Batch & Real-time Integration Patterns
- Integration Pattern: Batch Metadata Extraction
- Integration Pattern: Batch Summarization
- Integration Pattern: Real-Time Intent Classification
- Integration Pattern: Real-Time Retrieval Augmented Generation
- Operationalizing Generative AI Integration Patterns
- Embedding Responsible AI into your GenAI Applications
Review
“Generative AI Application Integration Patterns serves as a timely guide for navigating the nuanced landscape of integrating GenAI into existing business applications. This book is a well-written, engaging, and very relevant set of blueprints that technology and business leaders, as well as developers, should be aware of as they seek to integrate applications with the promise presented in GenAI. I encourage you to dive deep into the examples, reflect on the concepts presented in this book, and embark on the exciting journey of discovery and innovation in harnessing the potential of GenAI. The future of business is being shaped by AI, and this book is an essential companion on that path.”
Dr. Ali Arsanjani, Director of Applied AI Engineering, Google
“This book presents a timely and comprehensive resource for practitioners looking to effectively integrate generative AI into their applications. The book arrives at a pivotal moment in the evolution of Generative AI, not only demystifying the integration of LLMs into applications, but also emphasizing the ethical and responsible development of these technologies.
Bustos and Soria have expertly distilled complex concepts into actionable frameworks that can empower developers to navigate the challenges of GenAI integration effectively. Having pioneered scientific R&D leading global practices in Human Centered Meaning Aware Artificial Intelligence, AI Risk Management and Controls, and AI Business Performance Outcomes, we find their work particularly relevant in the context of advancing our understanding of AI systems beyond mere data-driven predictions to anticipating surprises in a rapidly evolving digital landscape for better business performance outcomes. In summary, Generative AI Application Integration Patterns serves as an essential guide for practitioners, offering a robust framework for harnessing the potential of generative AI while addressing critical ethical considerations.”
Dr. Yogesh Malhotra, Chairman at Global Risk Management Network, LLC and Founder of AWS Quantum Valley Network
About the Author
Juan Pablo Bustos is a seasoned technologist with over 20 years of experience in driving innovation and delivering impactful solutions across diverse industries. Juan brings expertise in solution architecture, product incubation, and integration, coupled with expertise in cloud computing, AI, and machine learning.
Luis Lopez Soria is an experienced software architect specialized in AI/ML. He has gained practical experience from top firms across heavily regulated industries (healthcare, and finance) as well as big tech. He brings a blended lens from his experience managing global partnerships, AI product development, and customer facing roles.
