Evaluating Gen AI Applications: A Practitioner’s Playbook for Testing What AI Actually Gets Right

A Gen AI system can produce an excellent answer and still fail in production. An agent can complete every individual step and still leave the business process in the wrong state. So what evidence is strong enough to release it—and what evidence should make you stop?

Evaluating Gen AI Systems and Agents — Third Editionis a practitioner’s guide to making evaluation a repeatable engineering discipline across Gen AI applications, RAG systems, coding and reasoning workflows, and AI agents.

The Third Edition develops that operating model through six connected themes.

1. Foundations of Evaluation — from uncertainty to an evidence system.
Start with the cost of flying blind, build a small evaluation suite, understand the evaluation stack and engineer a reproducible pipeline. Learn how to validate the evaluation harness itself, distinguish different dimensions of system quality and connect technical results to business outcomes rather than stopping at an aggregate score.

2. Building the Evaluation Assets — make what you know reusable.
Turn requirements, prompt behaviour and production failures into versioned evaluation contracts. Build governed datasets for development, capability discovery, regression protection, protected holdouts, production learning and simulation so that every model, prompt or application change does not force the organisation to rediscover the same failures.

3. Operating Evaluation in Production — evaluation does not stop at release.
Connect observability, run and trace evidence, thread and session evidence, production alerts, regression suites, release gates and human review. Learn how production failures become permanent tests, how mature capability evaluations graduate into regression protection and how teams preserve a reconstructable release decision when automation alone cannot settle the case.

4. Evaluating Complex Gen AI Systems — test the system, not only the final response.
Evaluate multi-step agents, synthetic-user simulations, RAG pipelines, code generation, reasoning workflows, model migrations and fine-tuning decisions. Separate step behaviour, run or trajectory quality, session outcome and external workflow state.

5. Risk, Trust and Governance — turn evaluation evidence into assurance.
Extend the evaluation system into fairness, representation and accessibility; adversarial testing and red teaming; governance, exceptions, decision rights and the 90-day operating roadmap. Security coverage uses OWASP guidance and MITRE ATLAS as coverage lenses, while governance evidence is mapped pragmatically to NIST AI RMF, the NIST Generative AI Profile, ISO/IEC 42001 and applicable regulatory obligations.

6. Implementing the Evaluation System — turn the architecture into something teams can build.
The final part converts the preceding nineteen chapters into an implementation-selection guide. Project managers learn what evidence to demand and how to challenge a proposed solution; developers get concrete implementation contracts and proof obligations.

Throughout the book, Meridian Insurance provides a continuous enterprise scenario. Failures move from development datasets into regression, production monitoring, human review, model migration, economics, adversarial testing and governance.

The Third Edition also includes eight optional browser-based companion labs covering the first evaluation suite, evaluation-stack architecture, dataset auditing, agent run and session evaluation, RAG diagnosis, code and reasoning verification, model-migration decisions and evaluation economics.

The central question is no longer simply:

“Did the model give the right answer?”

It is:

“What evidence would make us confident enough to let this system or agent act in real work"