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Based on what's actually in the book, here's what I'd write for the Leanpub \"About this Book\" section:

Most teams building Gen AI systems fail not because they chose the wrong model, but because they built the wrong architecture around it. Prompts hardcoded in application code. No evaluation harness. No gateway layer. No plan for what happens when the model provider goes down or silently changes behaviour. These are not edge cases — they are the default failure mode of every LLM system built without a production-first architecture.

This book exists to prevent those failures.

What this book covers

Building Production-Ready Gen AI Systems is a practitioner's reference for architects, senior engineers, and technical leads building LLM-based applications in production. It covers the full journey from assessing organisational readiness to deploying a system that is observable, auditable, and safe to change:

The AI Maturity Model — Four progressive levels (L1 Foundational through L4 Adaptive) that help you scope the right architectural ambition before committing to a design. Includes a pre-engagement assessment framework and capability coverage table.
Canonical Architecture — A six-layer reference blueprint (Clients, Gateway, Orchestration, Models, Data, Eval & Ops) covering every component a production LLM system requires, from the AI Gateway that makes costs auditable to the feedback loop that turns user corrections into ground truth.
Eight Design Patterns — Named, reusable patterns from Naive RAG to Multi-Agent Collaboration, each with When to Use criteria, Strengths and Limitations tables, and explicit trade-off analysis. Two patterns are documented with rejection rationale — showing when not to use a pattern is as important as showing when to use one.
Twelve Architecture Principles — Non-negotiable standards distilled from production failures, including Version Everything, Guardrails Are Not Optional, Own the Embedding Contract, and Isolate Tenant Data. Each principle includes a concrete example and an anti-pattern showing what goes wrong when the principle is ignored.
The 34-Point Engagement Checklist — A sequenced quality gate covering pre-engagement through production go-live, organised into three phases: Pre-Engagement (6 items), Architecture & Design (8 items), and Delivery & Handover (20 items). Every item requires evidence, not acknowledgement.

A complete worked case study applies every section of the framework to an autonomous M&A due diligence agent — a complex, regulated, multi-agent system that illustrates real architectural decisions including two patterns that were explicitly considered and rejected.

Who this book is for

This book is written for engineers and architects who are comfortable with distributed systems, REST APIs, and cloud infrastructure, but are newer to building LLM systems in production. It is also the right reference for technical leads who need a shared language and a structured framework for governing AI projects across teams.

It is explicitly scoped to LLM-based Gen AI systems — RAG pipelines, agentic workflows, and structured output systems built on foundation models. It does not cover computer vision, recommendation systems, or time-series forecasting.

What this book is not

This is not a beginner's guide to large language models. It does not teach you how to write prompts or choose between GPT-4o and Claude. It assumes you have already decided to build with LLMs and need to know how to build systems around them that survive production.

It is also not a textbook. The patterns and principles are synthesised from production deployments across financial services, healthcare, logistics, and public sector — they represent what has consistently worked and what has consistently failed, presented as a practitioner's reference rather than a statistically validated study.

Why this book now

The gap between \"it works in a demo\" and \"it works in production\" has never been larger or more expensive. Teams are reaching for multi-agent orchestration when a simple retrieval pipeline would solve the problem. Or they are building pipelines so primitive they cannot survive contact with real users. The maturity model, patterns, principles, and checklist in this book are the framework that closes that gap.

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Kick off your book project, get started with GhostAI, get better at marketing, or spend the day doing all three! Free live workshops on Zoom. Saturday, June 27, 2026.

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