AI-Native Systems Development

BUILD AI APPLICATIONS THAT WORK IN THE REAL WORLD - NOT JUST IN DEMONSTRATIONS. Foundation models make it easier than ever to create impressive AI prototypes. But turning a promising demo into a dependable application takes more than a clever prompt. It takes thoughtful architecture, careful testing, strong safeguards, and an understanding of what happens after launch. AI-Native Systems Development is a practical guide to designing, building, testing, and operating applications powered by foundation models. It shows you how to move beyond experimentation and engineer AI features into reliable products that real people can use. INSIDE, YOU'LL LEARN HOW TO:

• Understand foundation models, their capabilities, and their limitations.
• Design AI-native applications and choose where models belong in a system.
• Use prompting as an engineering discipline, not trial and error.
• Build retrieval-augmented generation (RAG) systems that bring relevant information into model responses.
• Connect models to tools and workflows, and design agent-based systems responsibly.
• Decide when fine-tuning is useful and how to evaluate model quality.
• Test for accuracy, consistency, safety, and failure before users encounter problems.
• Apply guardrails and protect applications against misuse and unreliable outputs.
• Manage inference performance, latency, and operating costs.
• Deploy, monitor, and continuously improve AI systems in production.

WHO SHOULD READ THIS BOOK? This book is written for software developers, backend and full-stack engineers, AI engineers, technical leads, solution architects, and product builders who want to create useful applications with foundation models. It is also valuable for founders and technical managers who need to understand the engineering decisions behind production AI products. You should be comfortable reading code and have some software development experience. The examples use Python, but the engineering principles apply across languages and technology stacks. You do not need a background in machine learning to get started. FROM AI EXPERIMENTS TO ENGINEERED SYSTEMS A model alone is not a product. Reliable AI applications depend on the systems around it: context, tools, validation, evaluation, security, monitoring, and human oversight. This book explains how those pieces work together-and why simplicity, measurement, and accountability matter at every stage. If you're ready to build beyond the demo and develop AI-powered applications with greater confidence, AI-Native Systems Development will help you approach the work with the mindset of an engineer.