Observability for LLM apps: what to log, privacy-safe telemetry, KPIs

Abstract

Large Language Model (LLM) applications increasingly form an integral part of enterprise software architecture, enabling conversational interfaces, intelligent assistant applications, and autonomous decision-support systems. While these applications provide tremendous flexibility and capability, their probabilistic nature, prompt dependency, and complex orchestration pipelines create new challenges for monitoring and reliability engineering. The traditional approach to observability, relying on logs, metrics, and traces, is found to be inadequate to measure semantic correctness, behavioral consistency, and governance risks associated with LLM applications. This study explores the concept of observability in large language model (LLM) applications from three different viewpoints: auditable data selection, privacy-preserving telemetry construction, and meaningful operational key performance indicator (KPI) definition. Following the best practices of software observability and MLOps, the study proposes a conceptual framework for model-agnostic observability in LLMs that covers the interaction layer, execution layer, performance layer, and safety layer. In particular, the study focuses on the application of privacy by design, including metadata-centric logging, selective redaction, and controlled access to telemetry data. Furthermore, this paper introduces a well-defined set of operational key performance indicators (KPIs) specific to large language model (LLM) applications, including reliability, performance efficiency, measures of output quality, and safety compliance. The above-mentioned parts of the framework enable the development of a well-structured framework for detecting faults, managing costs, as well as ensuring the reliability of LLMs. The above-mentioned framework makes it easier to implement LLMs at the enterprise level.