Name: Generative AI for Software Developers
Author: Saurabh Shrivastava Kamal Arora Ashutosh Dubey Dhiraj Thakur Sanjeet Sahay
ISBN: 9781806671182

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Generative AI for Software DevelopersSaurabh Shrivastava Kamal Arora Ashutosh Dubey Dhiraj Thakur Sanjeet Sahay

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Generative AI for Software Developers is your practical guide to mastering AI-powered development and staying ahead in a fast-changing industry. Through a structured, hands-on approach, this book helps you understand, implement, and optimize Generative AI in modern software engineering. From AI-assisted coding, debugging, and documentation to testing, deployment, and system design, it equips you with the skills to integrate AI seamlessly into your workflows. You’ll work with tools such as GitHub Copilot, Amazon Q Developer, and OpenAI APIs while learning strategies for prompt engineering, fine-tuning, and building scalable AI-powered applications. Featuring real-world use cases, best practices, and expert insights, this book bridges the gap between experimenting with AI and production deployment. Whether you’re an aspiring AI developer, experienced engineer, or solutions architect, this guide gives you the clarity, confidence, and tactical knowledge to thrive in the GenAI-driven future of software development. Armed with these insights, you’ll be ready to build, integrate, and scale intelligent solutions that enhance every stage of the software development lifecycle.

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Generative AI for Software Developers

Saurabh Shrivastava Kamal Arora Ashutosh Dubey Dhiraj Thakur Sanjeet Sahay

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文前
Foreword
About the Authors
About the Reviewers and Contributors
Preface
Chapter 1 – The Art and Science of Generative AI
What is Generative AI?
Generative AI Use Cases
Generative AI Benefits
Myths Around Generative AI
Challenges of Generative AI
Generative AI for Software Development
How Developers Should Evolve with Generative AI
Summary
Chapter 2 – Getting Started with Generative AI
Expanding Your Generative AI Knowledge: SLMs, LLMs, and LMMs
Foundation Models in Generative AI
How to Start with Generative AI
Code Generation Using Generative AI
Agentic AI Workflows
How Generative AI is Becoming Democratized
Summary
Chapter 3 – Generative AI Architecture Fundamentals
Understanding Generative AI Models Architecture
Category of Generative AI Models and Their Architecture
Approaches of Generative Models
Hyperparameter Tuning and Regularization
Model Evaluation Techniques
Choosing the Right Generative Model for Specific Use Cases
Best Practices for Model Evaluation
Summary
Chapter 4 – Generative AI in Software Development
Impact of Generative AI on Software Development
Essential Tools and Frameworks for Gen AI-Based Software Application Development
GenAI Ops: Operationalizing Generative AI Applications
Summary
Chapter 5 – Prompt Engineering For Software Developers
Why Prompt Engineering?
Prompt Techniques
Prompt Use Cases for the Software Development Lifecycle (SDLC)
Prompt Management Cycle and Best Practices
Prompt Engineering Tools
Summary
Chapter 6 – Integrating Generative AI into the Software Development Cycle
Industry Study on Developer Productivity with Generative AI
Transforming Software Development with Generative AI in the SDLC
Generative AI for Specific Programming Tasks
End-to-End AI Integration in the SDLC
Challenges and Tradeoffs in AI Integration
Key Metrics and KPIs for Measuring AI Impact
Next Steps: Sustaining and Expanding AI Integration
The Future Outlook
Summary
Chapter 7 – Ethical and Security Best Practices in Generative AI
Why the New Concerns?
Bias in AI-Generated Code
Model Architecture and Optimization Bias
Human Feedback Bias
Strategies to Mitigate Bias
Prompt Safety and Security for Responsible AI
Intellectual Property (IP) Considerations
Privacy Concerns in Generative AI
Key AI Laws and Guidelines
Security Risks in AI Applications
Security Architecture for Generative AI Apps
Guardrails for Secure Use of Generative AI Applications
Observability from an Ethical AI Perspective
Summary
Chapter 8 –Generative AI Application Architecture and Design
Principles of Generative AI Application Architecture
Text Generation Architecture
Text Summarization Architecture
Q&A (Question and Answer) App architecture
Chatbot Architecture
Image and Video Generation App Architecture
GenAI Architecture for Industry Use Cases
Summary
Chapter 9 – Reinforcement Learning and AI Agent Architecture Design
What is Reinforcement Learning?
Reinforcement Learning with Human Feedback
Automated Reinforcement Learning (AutoRL)
GenAI Agents
Agentic AI
Building an Intelligent Travel Assistant
GenAI Multi-Agent Systems
Function Calling with LLMs
Summary
Chapter 10 – Well-Architecting and Fine-tuning GenAI Application
What is Model Fine-Tuning?
Model evaluation
LLM Benchmarking
Building Well-Architected Gen AI Applications
Well-Architected Framework Pillers for GenAI Applications
Summary
Chapter 11 – Building a GenAI App from Prototype to Production
Building SkillGenie - Problem Statement
SkillGenie – Features
SkillGenie User Journey
System Design for SkillGenie
API Design
Prototype Development
Safe use of AI and content moderation
Enhancing SkillGenie outputs using Agentic AI
Production Launch
Post-Production Monitoring
Summary
Closing Thoughts
Other books you may enjoy
Solutions Architect Interview
Solutions Architect s Handbook
AWS for Solutions Architects

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Generative AI for Software Developers