"TruLens in Production: Feedback Functions, Scoring, and Continuous Evals"
Modern LLM systems rarely fail in obvious ways; they drift, degrade, and mislead under real traffic. This book is written for experienced ML, platform, and AI application engineers who need evaluation to function as a production capability, not a demo artifact. It treats TruLens as an operational framework for building trustworthy feedback loops around retrieval, generation, tool use, and agent behavior in live systems.
Readers will learn how to design evaluation pipelines end to end: instrument applications for evaluability, target runtime data with selectors, build and choose feedback functions, and interpret scores through sound aggregation strategies. The book covers the RAG triad, custom metrics, judge-model selection, human calibration, deferred evaluation workflows, persistent result storage, and debugging surfaces that connect dashboards back to trace-level evidence. The emphasis throughout is on trade-offs, failure modes, and decisions that matter in production.
Rather than repeating basic concepts, the book organizes the material as a progressive, systems-oriented guide for advanced practitioners. It assumes familiarity with LLM applications, observability concepts, and production deployment concerns, and focuses on the architectural and operational details needed to turn continuous evaluation into a reliable engineering practice.
