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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 &amp 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