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

About This Book

Most enterprise teams deploying LLMs in production have no systematic method for knowing whether their AI is actually working. They launch, they hope, and they wait for users to complain. This book exists to change that.

A Systematic Approach to Evaluating LLM Applications is a practitioner's guide for Chief AI Officers, engineering leads, and senior architects who are responsible for bringing LLM-powered products to production — and keeping them there. It covers the complete evaluation stack: from building the business case, through technical implementation, to board-level governance and regulatory compliance.

What you will learn:

The eight evaluation dimensions that together provide complete coverage of LLM application quality — functional correctness, safety, security, robustness, performance, context handling, quality, and RAGAS
How to implement LLM-as-Judge scoring at scale and calibrate it against human baselines
Advanced RAG evaluation patterns that go beyond standard RAGAS metrics, including chunk boundary failures, multi-hop retrieval gaps, and the lost-in-the-middle problem
How to build a structured red team programme with automated, internal, and external tiers — and integrate adversarial testing as a CI/CD release gate
Evaluation frameworks for agentic and multi-step AI systems, where compounding failure rates make single-turn evaluation insufficient
How to measure and remediate bias across five demographic dimensions, with statistical rigour and EU AI Act compliance in mind
The complete audit-ready evidence package required by the EU AI Act, ISO 42001, NIST AI RMF, GDPR, and sector-specific frameworks including SR 11-7 and DORA
Sector-specific evaluation requirements for BFSI, healthcare, and legal services — each with distinct failure modes, risk profiles, and regulatory obligations

Who this book is for:

This book is written for practitioners, not academics. It is for the engineering lead who needs to build an evaluation pipeline before the next sprint, the Chief AI Officer who needs to present quality evidence to the board, and the architect who needs to understand why their RAG application passes every demo but fails in production.

No prior experience with LLM evaluation is assumed. Readers who are new to the field will find a complete foundation. Readers who already have evaluation pipelines in place will find specific, actionable improvements — particularly in the chapters on agentic evaluation, advanced RAG patterns, bias measurement, and regulatory compliance.

What makes this book different:

Most resources on LLM evaluation describe the problem. This book describes the solution — in enough detail to implement it. Every chapter includes specific tools, concrete thresholds, diagnostic tables that map symptoms to root causes, and implementation patterns drawn from enterprise AI practice. The accompanying slide illustrations, originally developed for executive and engineering audiences, make the frameworks immediately communicable to stakeholders at every level of the organisation.

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