"Ragas for RAG: Measuring Retrieval, Faithfulness, and Answer Quality at Scale"
Modern retrieval-augmented generation systems fail in subtle ways: some miss the right evidence, some answer fluently without support, and others appear grounded while still failing the user's task. This book is written for experienced ML engineers, LLM application architects, and evaluation-focused researchers who need more than a single benchmark score. It offers a rigorous framework for dissecting RAG behavior with Ragas and turning evaluation into a practical engineering discipline.
Across the book, readers learn how to model evaluation data correctly, measure retrieval with context precision and recall, assess faithfulness as claim-level grounding, and evaluate answer quality through relevancy and correctness. It then shows how to interpret these metrics together to diagnose root causes, distinguish retriever defects from generator defects, and build iterative improvement loops. Operational chapters extend this work to large-scale batch evaluation, score aggregation, reproducibility, variance management, and version-aware infrastructure design.
The treatment is analytical, implementation-conscious, and aimed at advanced readers comfortable with RAG pipelines, LLM prompting, and experimental methodology. Rather than presenting evaluation as a checklist, the book develops a systems view: metrics become decision tools, datasets become contracts, and score patterns become evidence for engineering action."
