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 — and discovers 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.650+ pages. 25 chapters. 7 original production-tested frameworks: The Five Properties Model — 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) — move beyond token pricing to model the true cost of an LLM system, including hidden orchestration expenses, human review overhead, and failure remediation.LLM-FMEA — aerospace-grade pre-mortem methodology adapted for probabilistic AI. You will never deploy an LLM feature without first cataloging how it can break.Prompt Pattern Language (PPL) — elevate prompt design from craft to engineering discipline. Transform your team's prompt library from a collection of hacks into a governed, versioned architecture.The Eight-Layer Stack — the complete vertical blueprint of every production LLM system, from GPU memory constraints to compliance audit requirements.The Autonomy Gradient — calibrate how much freedom to give your AI agents, from "suggests" to "acts independently", with clear engineering controls at every level.The Implementation Playbook — 1,100+ lines of production-grade Python. Not pseudocode. RAG, multi-model routing, evaluation harness, circuit breaker, input guardrails, full test suite. Clone it. Ship it. This book is for you if: You are a senior engineer or architect building production LLM systemsYou are an engineering manager scaling an AI teamYou are a technical founder who needs to ship AI that actually worksYou have received the surprise invoice, debugged the hallucination at 2 AM, or stared at a system diagram wondering where it all went wrong 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 is not a book about language models.This is a book about the engineers who build systems around them — and the discipline those engineers need to do it responsibly.Topics: LLM engineering, production AI systems, machine learning architecture, LLM deployment, RAG systems, AI software design, large language model production, AI engineering best practices. Read more