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Generative AI for Software Developers

Generative AI for Software Developers

By : Saurabh Shrivastava, Kamal Arora, Ashutosh Dubey, Dhiraj Thakur, Sanjeet Sahay
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Generative AI for Software Developers

Generative AI for Software Developers

By: Saurabh Shrivastava, Kamal Arora, Ashutosh Dubey, Dhiraj Thakur, Sanjeet Sahay

Overview of this book

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.
Table of Contents (14 chapters)
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Preface
12
Closing Thoughts

Code Generation Using Generative AI

Generative AI has sparked widespread discussions about its potential impact on developers' jobs. While GenAI-based tools undoubtedly improve code generation, they remain far from replacing a developer's expertise, problem-solving capabilities, and contextual understanding. Instead of viewing these tools as disruptors, it's better to see them as enablers that can significantly enhance a developer's productivity.

Generative AI can assist developers in several use cases, including:

  • Natural language to code generation: Transforming written instructions into functional code.

  • Code completions and suggestions: Streamlining workflows by predicting and suggesting code snippets.

  • Bug detection and debugging: Identifying errors and suggesting fixes.

  • Security and vulnerability analysis: Ensuring the code adheres to security best practices.

  • Automated testing: Creating test cases to verify the functionality of code.

  • Code...

CONTINUE READING
83
Tech Concepts
36
Programming languages
73
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Generative AI for Software Developers
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