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Generative AI Application Integration Patterns

You're reading from   Generative AI Application Integration Patterns Integrate large language models into your applications

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Product type Paperback
Published in Sep 2024
Publisher Packt
ISBN-13 9781835887608
Length 218 pages
Edition 1st Edition
Languages
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Authors (2):
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Luis Lopez Soria Luis Lopez Soria
Author Profile Icon Luis Lopez Soria
Luis Lopez Soria
Juan Pablo Bustos Juan Pablo Bustos
Author Profile Icon Juan Pablo Bustos
Juan Pablo Bustos
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Table of Contents (13) Chapters Close

Preface 1. Introduction to Generative AI Patterns 2. Identifying Generative AI Use Cases FREE CHAPTER 3. Designing Patterns for Interacting with Generative AI 4. Generative AI Batch and Real-Time Integration Patterns 5. Integration Pattern: Batch Metadata Extraction 6. Integration Pattern: Batch Summarization 7. Integration Pattern: Real-Time Intent Classification 8. Integration Pattern: Real-Time Retrieval Augmented Generation 9. Operationalizing Generative AI Integration Patterns 10. Embedding Responsible AI into Your GenAI Applications 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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