Generative AI Application Integration Patterns
Generative AI Application Integration Patterns
Integrate large language models into your applications
About the Book
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.
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
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