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Evaluation-Driven AI Engineering: A Practical Guide to Testing and Improving LLM, RAG, and AI Agent Systems in Production
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Publisher: Independently Published
Pages: 330
Weight: 1.26
Height: 0.69 Width: 7.00 Depth: 10.00
Language: English
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.
Key FeaturesFramework-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.
Who This Book Is ForIdeal 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
Foundations of Evaluation-Driven AI Engineering
Engineering Evaluation Requirements and Datasets
Evaluation Methods, Metrics, and Experiment Design
Model-Based Evaluation and LLM-as-a-Judge
Evaluating Production LLM Applications
Evaluating Retrieval-Augmented Generation Systems
Evaluating AI Agents and Tool-Using Systems
Regression Testing and Evidence-Based Improvement
Evaluation in CI/CD and Production Delivery
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.
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