How can engineers tell whether an AI system is genuinely improving, rather than merely producing more convincing answers?
Eval-Driven Development presents a systematic approach to engineering reliable AI systems through measurable evaluation, reproducible experiments and evidence-based decisions.
Drawing on software testing, machine learning evaluation and modern AI engineering practices, this book explains how evaluation can become an integral part of the development lifecycle, from initial requirements to production monitoring.
Across 12 chapters, readers will explore:
Evaluation contracts, benchmarks and measurable success criteria.
Dataset design, sampling, leakage prevention and reproducibility.
Metrics, statistical uncertainty and human evaluation.
LLM-as-a-judge methods and evaluator calibration.
Retrieval-augmented generation (RAG) evaluation.
AI agent evaluation, tool-use traces and safety invariants.
Regression testing, CI/CD pipelines and release gates.
Production observability, monitoring and evaluation governance.
A recurring fictional case study, Mosaic, connects theory with practical engineering challenges through synthetic examples, executable laboratories and exercises.
Designed for AI engineers, data scientists, software developers, technical leads and postgraduate students, the book combines scientific foundations with practical implementation methods.
Companion materials provide code, exercises and worked solutions organised by chapter.
Rather than promising flawless AI systems, Eval-Driven Development teaches readers how to establish evidence, identify limitations and continuously improve reliability.
