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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
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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 FREE CHAPTER
2. Chapter 1: Introduction to Generative AI-Powered Assistants 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

What is a prompt?

As specified earlier, LLMs are pretrained on publicly available large datasets, which makes them very powerful and versatile. These LLMs typically have billions of parameters that can be used to solve multiple tasks out of the box, without the need for additional training.

Users just need to ask the right question with relevant context to get the best output from an LLM. The plain text comments/questions that act as an instruction to an LLM are called prompts and the technique of asking the right questions with corresponding context is called prompt engineering. While interacting with LLMs, providing prompts with precise information and, if required, supplementing with additional relevant context, is very important in order to get the most accurate results. The same is true while interacting with code assistants as most of the code assistants are integrated with an LLM. As a user, while interacting with a code assistant, you should provide prompts with simple,...

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