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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
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Index

Integration Pattern: Real-Time Intent Classification

In previous chapters, we discussed the batch-processing integration pattern, where we focused on efficiently processing large volumes of data and generating data to be used by downstream systems. In this chapter, we will shift our focus to real-time integration patterns.

Real-time interactions require applications to be optimized for latency, rather than processing large batch requests efficiently. In other words, we need to ensure that the output is generated as quickly as possible to provide an optimized user experience. The most common use case for this pattern is real-time agents exposed through chat or voice interfaces.

Let’s consider an intent classification use case, which is a common scenario for chatbots. In this context, an artificial intelligence (AI) system is responsible for identifying the user’s intent, such as checking a balance, scheduling an appointment, or making a purchase. Based on the...

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