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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
11
Other Books You May Enjoy
12
Index

Embedding Responsible AI into Your GenAI Applications

In the previous chapters, we explored various integration patterns and operational considerations for leveraging Generative AI (GenAI) models like Google Gemini on Vertex AI. As we implement these powerful technologies, it’s crucial to address the ethical implications and responsibilities that come with building and deploying AI models that will be added to your applications. This chapter will focus on best practices for responsible AI, ensuring that our GenAI applications are fair, interpretable, private, and safe.

In this chapter, we’ll cover:

  • Introduction to responsible AI
  • Fairness in GenAI applications
  • Interpretability and explainability
  • Privacy and data protection
  • Safety and security in GenAI systems
  • Google’s approach to responsible AI
  • Anthropic’s approach to responsible AI
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Generative AI Application Integration Patterns
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