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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
Author Profile Icon Bunny Kaushik
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

Code generation with LLMs – unleashing the power of AI-driven development

As the field of AI continues to evolve, one of the most exciting and promising areas is the use of LLMs for code generation, especially in the case of developer productivity gains. Customers can leverage state-of-the-art LLMs available on Amazon Bedrock to generate high-quality code, revolutionizing the way developers approach software development.

The code generation process

The code generation process with Amazon Bedrock is straightforward and user-friendly. Developers can interact with the platform through a web-based interface or via an API, as discussed in the previous chapters. The process typically involves the following steps:

  1. Problem description: The developer provides a natural language description of the desired functionality or task that they want the code to perform.
  2. Context and constraints: The developer can optionally provide additional context, such as programming language...
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