Building with GenAI: What Every Developer Should Know
In today’s fast-moving software landscape, Generative AI (GenAI) is reshaping how developers build, debug, and design applications. Instead of only writing code, developers are now engaging with large language models (LLMs), designing prompt workflows, and crafting intelligent systems that learn and evolve.
But while GenAI brings tremendous opportunities, getting started can feel overwhelming.
That’s where the new book “Generative AI for Software Developers” comes in—crafted to provide practical guidance and real-world applications to help developers build confidently in the GenAI era.
🔍 What’s Inside the Book?
This isn’t just a collection of AI buzzwords. The book builds up from foundational GenAI concepts, breaking down topics like:
What makes LLMs different from traditional models
How embeddings and attention mechanisms work
Why prompt engineering is central to AI system design
It walks through how to apply GenAI throughout the software development lifecycle (SDLC)—from code generation to intelligent testing and debugging.
You'll learn patterns like:
Multi-step prompting
Few-shot and chain-of-thought techniques
Prompt frameworks tailored for developers
💡 These prompt patterns aren’t theoretical—they’re real tools you can use to increase productivity and improve model responses.
🏗️ Architecting GenAI-Powered Applications
Beyond the prompt layer, the book dives into how to architect real-world GenAI systems, covering:
Retrieval-Augmented Generation (RAG) for document-aware assistants
Agentic AI architectures for autonomous workflows
Secure and scalable GenAI system design
It emphasizes how developers and architects can go beyond chatbots to embed intelligence into enterprise systems, customer support tools, or automation pipelines.
⚙️ Dev Tools and Cloud Integration
The book also highlights modern GenAI tools including:
Amazon Q Developer for AI-assisted code authoring
OpenAI developer APIs for embedding intelligence
Amazon Bedrock and SageMaker for hosting, fine-tuning, and observability
Whether you're a startup developer or a cloud engineer at an enterprise, this section helps you understand how to add GenAI without disrupting your stack.
🧪 Hands-On Examples
Each chapter includes hands-on code samples with a GitHub repo, showing how to:
Build an AI-powered assistant using RAG
Implement agent feedback loops
Use GenAI in CI/CD, QA, or performance testing
The book even explores RLHF (Reinforcement Learning from Human Feedback)—the method used to fine-tune models like ChatGPT for better alignment.
👩💻 Who Should Read This?
This book is designed for:
Software Developers looking to modernize workflows
Cloud Architects designing scalable GenAI services
Technical Leaders exploring AI-first product strategies
Whether you're integrating GenAI into your product roadmap or exploring AI augmentation in internal tools, this guide will give you a structured path forward.
📚 Ready to Dive In?
Generative AI for Software Developers is more than just theory—it’s a toolkit for building intelligent systems, enhancing your development workflows, and staying ahead in a rapidly evolving industry.
👉 Get the eBook now at 40% OFF on BitMaple Store:
https://bitmaple.com/#book-publishing-details
📦 Buy on Amazon: https://www.amazon.com/gp/product/B0DYZV9X9N
🌏 For India/APAC via Pothi: https://store.pothi.com/book/saurabh-shrivastava-generative-ai-software-developers/