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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

Introduction to responsible AI

Responsible AI is an approach to developing and deploying AI systems that prioritizes ethical considerations, transparency, and accountability.

As GenAI models and applications such as Google’s Gemini, OpenAI’s GPT, and Anthropic’s Claude become increasingly powerful and widely used, it’s essential to ensure that these systems are designed and implemented in ways that benefit society while minimizing potential harm. Let’s explore, at a high level, the key aspects of implementing responsible AI in your systems. We will also talk about how over-indexing on the following topics can have a negative effect on innovation:

  • Fairness: Achieving fairness in AI systems is a crucial goal that requires thoughtful design and implementation throughout the entire AI lifecycle. Several key factors contribute to making AI systems fair. First and foremost is the quality and diversity of the training data. Ensuring that...
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