Engineering LLM Systems: From prototype to production at planet scale

By Erik Gieske

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 - 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 - negotiate trade-offs between
capability, latency, cost, reliability, and safety
Total Cost of Intelligence (TCI) - model the true cost
beyond token pricing: orchestration, human review, failure
remediation
LLM-FMEA - aerospace-grade pre-mortem methodology adapted
for probabilistic AI
Prompt Pattern Language (PPL) - elevate prompt design from
craft to governed engineering discipline
The Eight-Layer Stack - complete vertical blueprint from
GPU memory to compliance audit
The Autonomy Gradient - calibrate AI agent freedom with
clear engineering controls at every level

Chapter 25: The Implementation Playbook - This alone is worth
the price.

Every framework in this book is implemented in production-grade
Python. Not pseudocode. Not fragments. A complete, modular reference
codebase you can clone today and ship tomorrow.

Hybrid RAG Pipeline - dense + sparse retrieval,
Reciprocal Rank Fusion, cross-encoder reranking, source attribution
Multi-Model Router - cost-aware query routing with
automatic fallback chain across model tiers
Evaluation Harness - CI/CD deployment gate with
LLM-as-Judge, Five Properties measurement, pass/fail decision
Circuit Breaker + Cost Governor - four-dimensional budget
control (per-request, per-session, per-minute, daily) - the system
that prevents the $47,000 invoice
Input Guardrails - prompt injection detection, PII
redaction, toxicity filtering
Full Test Suite - 5 test classes against real LLM-FMEA
failure modes

A senior engineer who uses this codebase as a starting point saves
days of architecture work. At $29.99, that is the highest ROI
technical book purchase you will make this year.

This book is for you if:

You have received the surprise invoice after a production
LLM deployment
You have debugged a hallucination at 2 AM
You are scaling an AI team and need shared engineering language
You are a technical founder who needs to ship AI that
actually works
You are ready for the most consequential engineering role
of the decade

Chapter 24, The Engineer's Responsibility, confronts what
every other AI book avoids: a modern Hippocratic Oath for LLM
engineers, frameworks for regulatory future-proofing, and a
philosophy of stewardship.

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.

• Format: Paperback
• Language: English
• ISBN: B0GXDRS2QS
• ISBN13: 9798257510281
• Release Date: April 2026
• Publisher: Independently Published
• Length: 656 Pages
• Weight: 1.90 lbs.
• Dimensions: 1.3" x 6.0" x 9.0"