Evaluation-Driven AI Engineering: A Practical Guide to Testing and Improving LLM, RAG, and AI Agent

Building an AI system is easy to demonstrate. Proving that it is reliable enough for production is much harder.

A prompt may seem better while introducing new failures. A RAG pipeline may retrieve useful information but still generate unsupported answers. An AI agent may complete a task while choosing the wrong tool, increasing cost, or creating unacceptable risk.

Evaluation-Driven AI Engineering shows you how to replace guesswork with measurable evidence.

Written for developers, engineers, and technical practitioners who are new to formal AI evaluation, this practical guide explains how to test, measure, compare, and improve LLM, RAG, and AI agent systems step by step. You do not need prior experience with evaluation frameworks, LLM-as-a-judge systems, regression suites, or production AI quality programs. Basic familiarity with programming and modern AI applications is enough to get started.

Rather than focusing on abstract theory, the book uses practical examples, compact code, rubrics, evaluation datasets, test cases, failure analysis, exercises, and reusable checklists.

• Framework-neutral AI evaluation methods

• Practical testing for LLM, RAG, and AI agent systems

• Evaluation datasets, metrics, rubrics, and test cases

• LLM-as-a-judge design, calibration, and validation

• RAG retrieval, groundedness, citation, and generation evaluation

• AI agent tool-use and trajectory testing

• Regression testing and evidence-based system comparison

• CI/CD evaluation gates and production monitoring

• Reusable templates, checklists, metrics, and failure taxonomies

Learn how to define measurable quality requirements, build representative evaluation datasets, choose deterministic and semantic evaluators, measure groundedness and task success, validate LLM judges, evaluate retrieval and generation separately, test agent tool behavior, compare changes against reliable baselines, detect regressions, create release gates, monitor production drift, and turn real failures into stronger future tests.

Ideal for:

• Software engineers working with LLM applications

• Developers building RAG systems

• Engineers creating AI agents and tool-using workflows

• Technical practitioners new to AI evaluation

• Teams introducing quality gates for production generative AI

• Self-learners with basic programming knowledge

1. Foundations of Evaluation-Driven AI Engineering

2. Engineering Evaluation Requirements and Datasets

3. Evaluation Methods, Metrics, and Experiment Design

4. Model-Based Evaluation and LLM-as-a-Judge

5. Evaluating Production LLM Applications

6. Evaluating Retrieval-Augmented Generation Systems

7. Evaluating AI Agents and Tool-Using Systems

8. Regression Testing and Evidence-Based Improvement

9. Evaluation in CI/CD and Production Delivery

10. Production Evaluation and Continuous Improvement

Stop relying on a few promising examples to decide whether your AI system is improving.

Start building stronger evaluation practices today and learn how to test, improve, and ship production AI systems with greater confidence.