Overview

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.

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