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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

Product type Paperback

Published in Aug 2024

Publisher Packt

ISBN-13 9781835089149

Length 416 pages

Edition 1st Edition

Tools

AWS

Concepts

GPT/LLMs

Authors (2):

Behram Irani

Rahul Sonawane

View More author details

Table of Contents (23) Chapters

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

Why subscribe?

22. Other Books You May Enjoy

Part 3:Advanced Assistant Features

In this part, we will explore some of the advanced features of Amazon Q Developer that enhance developer productivity by providing capabilities beyond the basic auto-code generation.

This part contains the following chapters:

Chapter 10, Customizing Code Recommendations
Chapter 11, Understanding Code References
Chapter 12, Simplifying Code Explanation, Optimization, Transformation, and Feature Development
Chapter 13, Simplifying Scanning and Fixing Security Vulnerabilities in Code

The rest of the chapter is locked

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Authors (2)

Behram Irani

Behram Irani

Behram Irani is currently a technology leader at Amazon Web Services (AWS), specializing in data, analytics, and AI/ML. With over 20 years in the tech industry, he has helped organizations ranging from start-ups to large-scale enterprises modernize their data platforms. In the last 7 years at AWS, Behram has been a thought leader in the data, analytics, and AI/ML space. He has published a book and multiple papers and has led digital transformation efforts for many companies worldwide. Behram holds a Bachelor of Engineering in Computer Science from the University of Pune and an MBA from the University of Florida. He is also the author of Modern Data Architecture on AWS.

See other products by Behram Irani

Rahul Sonawane

Rahul Sonawane

Rahul Sonawane is currently a specialist solution architect at AWS specializing in data, analytics, and AI/ML. With over 24 years of technology experience, he has worked across multiple industries globally, including retail, consulting, and large-scale enterprises. Before joining AWS, Rahul worked on Google Cloud Platform for 2 years. For the last 7 years at AWS, he has been helping organizations modernize their data platforms. Rahul has published multiple public blog posts and articles focusing on data, analytics, and AI/ML. He holds a Master of Engineering in Computer Science from the University of Pune with a specialization in data warehouse technologies.

See other products by Rahul Sonawane

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