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Generative AI-Powered Assistant for Developers

You're reading from   Generative AI-Powered Assistant for Developers Accelerate software development with Amazon Q Developer

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Product type Paperback
Published in Aug 2024
Publisher Packt
ISBN-13 9781835089149
Length 416 pages
Edition 1st Edition
Tools
Concepts
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Authors (2):
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Behram Irani Behram Irani
Author Profile Icon Behram Irani
Behram Irani
Rahul Sonawane Rahul Sonawane
Author Profile Icon Rahul Sonawane
Rahul Sonawane
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Table of Contents (23) Chapters Close

Preface 1. Part 1:Generative AI-Powered Assistant
2. Chapter 1: Introduction to Generative AI-Powered Assistants FREE CHAPTER 3. Chapter 2: Introducing and Setting Up Amazon Q Developer 4. Part 2:Generate Code Recommendations
5. Chapter 3: Understanding Auto-Code Generation Techniques 6. Chapter 4: Boost Coding Efficiency for Python and Java with Auto-Code Generation 7. Chapter 5: Boost Coding Efficiency for C and C++ with Auto-Code Generation 8. Chapter 6: Boost Coding Efficiency for JavaScript and PHP with Auto-Code Generation 9. Chapter 7: Boost Coding Efficiency for SQL with Auto-Code Generation 10. Chapter 8: Boost Coding Efficiency for Command-Line and Shell Script with Auto-Code Generation 11. Chapter 9: Boost Coding Efficiency for JSON, YAML, and HCL with Auto-Code Generation 12. Part 3:Advanced Assistant Features
13. Chapter 10: Customizing Code Recommendations 14. Chapter 11: Understanding Code References 15. Chapter 12: Simplifying Code Explanation, Optimization, Transformation, and Feature Development 16. Chapter 13: Simplifying Scanning and Fixing Security Vulnerabilities in Code 17. Part 4:Accelerate Build on AWS
18. Chapter 14: Accelerate Data Engineering on AWS 19. Chapter 15: Accelerate Building Solutions on AWS 20. Chapter 16: Accelerate the DevOps Process on AWS 21. Index 22. Other Books You May Enjoy

Chain-of-thought prompts for auto-code generation

Chain-of-thought prompts for auto-code generation refer to a technique where the user employs a combination of single-line or multi-line prompts to provide step-by-step instructions. Code assistants then use an LLM to automatically generate code for each step individually. Users can use multiple natural language prompts that can link together to solve complex requirements. These prompts can be chained together to guide the model to produce relevant targeted code. It’s an effective technique to divide complex coding tasks into smaller code fragments by providing simple prompts to the code assistant one at a time. The code assistant can use each prompt to generate more tailored code fragments. Ultimately, all the code fragments can be used as building blocks to solve the complex requirement.

Here are some key points about chain-of-thought prompts for auto-code generation:

  • Chain-of-thought prompts are a technique in which...
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