Generative AI Engineering for Developers: Build Intelligent Applications with Large Language Models, Retrieval Systems, AI Agents and Production-Ready Workflows - Softcover

Inhaltsangabe

Generative AI is transforming software development at an unprecedented pace. From intelligent assistants and AI-powered search engines to autonomous agents and enterprise knowledge systems, organizations are rapidly adopting AI to build smarter products and automate complex workflows.

Generative AI Engineering for Developers is a practical guide that teaches you how to design, build, deploy, and operate production-ready AI applications. Rather than focusing only on prompts or model APIs, this book explores the complete engineering lifecycle required to create reliable, scalable, secure, and maintainable AI systems. This book goes beyond theory and AI hype to provide a real-world engineering perspective. Drawing from modern software engineering practices, it explains how Large Language Models, Retrieval-Augmented Generation (RAG), vector databases, AI agents, evaluation frameworks, monitoring systems, and LLMOps work together to power modern intelligent applications. Every concept is presented with practical implementation strategies and production-focused guidance.

Modern Generative AI systems combine multiple technologies, including Large Language Models, embeddings, vector databases, retrieval pipelines, agent frameworks, memory systems, cloud infrastructure, and monitoring platforms. When integrated correctly, these technologies enable applications that can understand language, retrieve knowledge, automate tasks, generate content, and support intelligent decision-making.

• Foundations of Generative AI Engineering
• Working with Large Language Models
• Prompt Engineering and Context Design
• Building AI-Powered Applications
• Retrieval-Augmented Generation (RAG)
• Embeddings and Vector Databases
• Enterprise Knowledge Systems
• AI Agents and Autonomous Workflows
• Evaluation and Hallucination Detection
• Security, Governance, and Responsible AI
• Production Deployment and Scaling
• Monitoring, Observability, and LLMOps
• Real-World Generative AI Projects

This book is ideal for software developers, AI engineers, backend developers, full-stack developers, technical architects, engineering managers, and technology professionals who want to move beyond AI experimentation and build production-ready intelligent systems. Whether you are new to Generative AI or looking to strengthen your engineering expertise, this guide provides a structured and practical learning path.

Generative AI is rapidly becoming a core component of modern software development. Companies across every industry are actively investing in AI-powered solutions and searching for professionals who can build and operate them effectively. The demand for Generative AI engineering skills has never been higher. Instead of spending months piecing together information from scattered tutorials, research papers, documentation, and online courses, this book provides a clear roadmap that accelerates your learning and helps you focus on the skills that matter most. In a single resource, you will gain practical knowledge of Large Language Models, prompt engineering, RAG systems, vector databases, AI agents, evaluation frameworks, security practices, deployment strategies, monitoring systems, and LLMOps principles, the essential technologies driving the next generation of software applications.

The future of software belongs to developers who can build intelligent systems that understand, reason, retrieve knowledge, and automate complex tasks. If you're ready to transform your AI ideas into real-world applications and become a highly valuable Generative AI engineer, this book will show you exactly how to do it. Start building the future of intelligent software today.

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