"Phoenix for RAG Debugging: Traces, Retrieval Quality, and Hallucination Triage"
Modern RAG systems rarely fail in obvious ways: a wrong answer may stem from weak retrieval, broken trace visibility, poor context use, or genuine hallucination. This book is written for experienced engineers, ML practitioners, and platform teams who need more than dashboards and intuition. It presents Phoenix as a disciplined debugging and evaluation workflow for diagnosing complex RAG behavior with evidence, precision, and operational rigor.
Across the book, readers learn how to instrument RAG applications with high-fidelity traces, interpret spans as executable narratives of a request, evaluate retrieval quality, and judge whether responses are grounded in retrieved context. The coverage connects single-run forensics to large-scale cohort analysis, then extends into repeatable triage playbooks, durable labeling strategies, and experiment design for validating fixes. By the end, readers can move systematically from symptom to root cause to regression-resistant improvement.
The book assumes familiarity with RAG architectures, LLM application design, and production observability concepts. Its distinguishing strength is the integration of tracing, retrieval analysis, hallucination triage, and experimental validation into one coherent engineering method, with version-aware guidance that reflects modern Phoenix capabilities rather than outdated practice.
