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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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Other Books You May Enjoy
12
Index

Summary

In this chapter, we discussed the integration pattern for building a real-time intent classification system using Google’s Gemini Pro generative AI model. We started by introducing the concept of real-time integration patterns, which prioritize low latency over efficiency and volume, as opposed to batch-processing integration patterns.

The use case we developed is an e-commerce company that wants to improve its customer service experience by automatically categorizing incoming customer inquiries into predefined intents, such as order status, product inquiry, return request, or general feedback. This classification can then be used to route the inquiry to the appropriate team or provide automated responses for common issues.

The architecture proposed is a serverless, event-driven architecture on Google Cloud, consisting of an ingestion layer (Cloud Functions), an AI processing layer (Vertex AI with Gemini Pro), an intent classification model, orchestration and...

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