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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
Tools
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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

Multimodal design patterns

With multimodal design patterns, we integrate different modalities, such as text, images, audio, and so on. With the multimodal models available, the ability to generate, manipulate, and understand images from text or other input modalities has become increasingly important in a wide range of applications, from creative design to scientific visualization and beyond.

Numerous patterns can be created with multimodal models. In this section, we are going to cover some of the common patterns.

Text-to-image

With a text-to-image pattern, you provide the text as a prompt to the model. The model will then generate an image based on that prompt, as shown in Figure 9.3.

Figure 9.3 – A text-to-image pattern

Figure 9.3 – A text-to-image pattern

Parameters

At the core of image generation models are a set of customizable inference parameters and controls that allow users to get the desired image from the model. Let us look at these parameters:

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