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Generative AI Application Integration Patterns

Generative AI Application Integration Patterns

By : Juan Pablo Bustos, Luis Lopez Soria
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Generative AI Application Integration Patterns

Generative AI Application Integration Patterns

By: Juan Pablo Bustos, Luis Lopez Soria

Overview of this 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 (13 chapters)
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7
Integration Pattern: Real-Time Intent Classification
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11
Other Books You May Enjoy
12
Index

Use case definition

Let’s consider a scenario where we’re working with an e-commerce company that wants to improve its customer service experience. The company receives a large volume of customer inquiries through various channels, such as email, chat, and social media. Currently, these inquiries are handled manually by a team of customer service representatives, which can be time-consuming and prone to inconsistencies.

By integrating intent classification into customer engagement flows, companies can optimize their customer service operations. This advanced natural language processing technique automatically categorizes incoming customer inquiries into predefined intents, such as “order status,” “product inquiry,” “return request,” or “general feedback.” The classification layer acts as an intelligent entry point for customer service interactions, enabling more efficient and accurate routing of inquiries.

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