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  • Book Overview & Buying Generative AI for Software Developers
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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

Hyperparameter Tuning and Regularization

For a software developer, hyperparameter tuning balances model output based on the use case, enabling control over response quality and creativity. Regularization ensures that models generalize well, especially when fine-tuning, and remain responsive without unnecessary compute overhead. Together, these techniques empower developers to create GenAI applications that are efficient, engaging, and reliable, ultimately enhancing user satisfaction and achieving business goals.

Hyperparameter Tuning in GenAI

In GenAI, hyperparameter tuning is crucial for controlling the model's output to match specific application needs. As software developers, you can use these settings to adjust the model's behavior, whether for generating consistent, factual answers or creative, diverse content. Following are some of the Key Hyperparameters for Developers:

  • Temperature: Controls randomness in model output. For factual applications, developers...

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