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Generative AI with Amazon Bedrock

You're reading from   Generative AI with Amazon Bedrock Build, scale, and secure generative AI applications using Amazon Bedrock

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Product type Paperback
Published in Jul 2024
Publisher Packt
ISBN-13 9781803247281
Length 384 pages
Edition 1st Edition
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Authors (2):
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Shikhar Kwatra Shikhar Kwatra
Author Profile Icon Shikhar Kwatra
Shikhar Kwatra
Bunny Kaushik Bunny Kaushik
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Bunny Kaushik
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Table of Contents (18) Chapters Close

Preface 1. Part 1: Amazon Bedrock Foundations FREE CHAPTER
2. Chapter 1: Exploring Amazon Bedrock 3. Chapter 2: Accessing and Utilizing Models in Amazon Bedrock 4. Chapter 3: Engineering Prompts for Effective Model Usage 5. Chapter 4: Customizing Models for Enhanced Performance 6. Chapter 5: Harnessing the Power of RAG 7. Part 2: Amazon Bedrock Architecture Patterns
8. Chapter 6: Generating and Summarizing Text with Amazon Bedrock 9. Chapter 7: Building Question Answering Systems and Conversational Interfaces 10. Chapter 8: Extracting Entities and Generating Code with Amazon Bedrock 11. Chapter 9: Generating and Transforming Images Using Amazon Bedrock 12. Chapter 10: Developing Intelligent Agents with Amazon Bedrock 13. Part 3: Model Management and Security Considerations
14. Chapter 11: Evaluating and Monitoring Models with Amazon Bedrock 15. Chapter 12: Ensuring Security and Privacy in Amazon Bedrock 16. Index 17. Other Books You May Enjoy

Ethical practices

There have been rapid advancements in GenAI, but at the same time, they raise new challenges and risks. Some of these are as follows:

  • Would my data be used with the provider?
  • Could it hurt the legal rights of the company?
  • Will the model hallucinate and provide non-sensical, biased, or factually incorrect responses in production?
  • Would the GenAI models inadvertently use or reproduce intellectual property, such as copyrighted text or images, during training or generation?

Let us cover these challenges and best practices that can be adopted.

Veracity

Veracity, or the truthfulness and accuracy of information generated by AI models, is a crucial aspect of ethical practices in the field of GenAI. When models produce outputs that are verifiably false or hallucinated, it can lead to the spread of misinformation and undermine trust in the technology. One common example of hallucinations is when an AI model is asked to provide information about...

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