Engineering LLM Systems: From prototype to production at planet scale

The book the AI industry didn't know it was waiting for.

Every week, another company burns through six figures moving an LLM prototype to production. They discover - too late - that calling an API is not engineering. That a clever prompt is not architecture. That a working demo is not a system.

This book is the discipline they were missing.

Engineering LLM Systems is the first comprehensive field manual for LLM Engineering - a discipline at the intersection of software architecture, probabilistic systems design, cost engineering, safety governance, and ethical responsibility. Not a tutorial. Not a tips collection. A complete operating system for the engineer who builds production AI.

Across 25 chapters and 650+ pages, Erik Gieske introduces seven original, production-tested frameworks:

THE FIVE PROPERTIES MODEL gives you a shared language to negotiate trade-offs between capability, latency, cost, reliability, and safety - with your product team, your CFO, and your compliance officer.

TOTAL COST OF INTELLIGENCE (TCI) moves beyond token pricing to model the true cost of an LLM system - including hidden orchestration expenses, human review overhead, failure remediation, and context window accumulation.

LLM FAILURE MODE AND EFFECTS ANALYSIS (LLM-FMEA) adapts the rigorous pre-mortem methodology from aerospace engineering to probabilistic AI. After this chapter, you will never deploy an LLM feature without first cataloging how it can break.

THE PROMPT PATTERN LANGUAGE (PPL) elevates prompt design from craft to engineering discipline - transforming your team's prompt library from a collection of hacks into a governed, versioned architecture.

THE EIGHT-LAYER STACK gives you the complete vertical blueprint of every production LLM system - from GPU memory constraints to compliance audit requirements.

THE AUTONOMY GRADIENT provides the framework for calibrating how much freedom to give your AI agents - from "suggests" to "acts independently" - with clear engineering controls at every level.

THE IMPLEMENTATION PLAYBOOK (Chapter 25) is the proof: 1,100+ lines of production-grade Python. Not pseudocode. A complete reference implementation - RAG, multi-model routing, evaluation harness, circuit breaker, input guardrails, full test suite. Clone it. Ship it.

Chapter 24, The Engineer's Responsibility, confronts what every other AI book avoids: to what end? A modern Hippocratic Oath for LLM engineers. Frameworks for regulatory future-proofing. A philosophy of stewardship - not technician building features, but guardian building infrastructure that must endure.

This book is for: Senior engineers and architects building production LLM systems. Engineering managers scaling AI teams. Technical founders who need to ship AI that actually works. Developers ready to step into the most consequential engineering role of the decade.

If you have ever received the surprise invoice, debugged the hallucination at 2 AM, or stared at a system diagram wondering where it all went wrong - this book was written for you.

This is not a book about language models. This is a book about the humans who build systems around them - and the discipline those humans need to do it responsibly.