"OpenLIT for GenAI: OpenTelemetry-Style Observability for LLM Apps"
Modern LLM systems fail in ways that ordinary application monitoring cannot explain: a prompt path degrades, a retrieval branch stalls, a tool call breaks context, or costs surge without a clear cause. This book is for experienced engineers, platform teams, SREs, and advanced AI practitioners who need deep, production-grade observability for GenAI systems. It frames OpenLIT not as a dashboard product, but as an OpenTelemetry-native observability layer for understanding how real LLM applications behave under load and failure.
Readers will learn how to instrument LLM applications with OpenLIT SDKs, model traces across agents, tools, retrieval, and multi-service workflows, and design metrics and logs that reveal latency, throughput, token, and cost behavior. The book also covers context propagation, GenAI semantic conventions, collector-centric telemetry pipelines, storage and backend architecture, and practical investigation workflows that connect fleet-level anomalies to single-trace explanations. By the end, readers will be able to build, operate, and evolve a robust observability practice for complex GenAI systems.
The treatment is rigorous, architecture-first, and version-aware, with careful attention to compatibility, failure modes, and operational trade-offs. Familiarity with distributed systems, modern observability, and LLM application development is assumed. Rather than offering shallow setup instructions, the book provides a cohesive mental model for deploying OpenLIT in serious engineering envir
