Architecting Generative AI Applications: Build, deploy, and scale production-ready GenAI systems with LLMOps best practices

Build Generative AI for the Real World

Architecting Generative AI Applications is a focused, production-minded guide for teams that want more than a promising prototype. Leonid Kuligin concentrates on the hard part of GenAI work: making systems dependable, secure, measurable, and maintainable once they leave the demo stage.

The book moves from early product thinking into the architecture and operations decisions that shape successful deployments. You’ll find guidance on evaluating LLMs, structuring retrieval-augmented generation systems, working with vector databases, and thinking through agentic and memory-based patterns. From there, the emphasis shifts to the engineering discipline required to support real usage: testing, deployment, observability, reliability, security, and the practical realities of LLMOps.

What the Book Covers

• Building prototypes with a path to production
• Evaluating GenAI applications with metrics and human review
• Designing modern AI architectures such as RAG and agentic systems
• Applying code quality and testing practices to GenAI projects
• Using DevOps, MLOps, and LLMOps principles for deployment and scale
• Strengthening observability, reliability, and Responsible AI practices
• Running A/B tests to measure impact more confidently

Who Will Find It Useful

This ebook is aimed at technical leaders, AI engineers, data scientists, software engineers, and architects who are building generative AI applications. It also has clear value for engineering managers, product leaders, and decision-makers who need a grounded view of what it takes to bring GenAI into production without losing control of quality or reliability.

A Practical Perspective on GenAI

Rather than treating generative AI as a novelty, this title frames it as a systems problem: one that requires thoughtful design, careful measurement, and disciplined operations. That makes it a strong fit for readers looking for a serious, implementation-aware book on modern AI architecture.