Generative AI Application Integration Patterns: Integrate large language models into your applications
Generative AI Application Integration Patterns: Integrate large language models into your applications
English | ISBN: 1835887600 | 2024 | EPUB | 218 pages | 5 MB
Juan Pablo Bustos, Luis Lopez Soria, “Generative AI Application Integration Patterns: Integrate large language models into your applications”
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
