This article explores how OpenTelemetry Semantic Conventions for Generative AI provide deep visibility into LLM-powered applications by standardizing the recording of model calls, tool invocations, and token exchanges. It provides a practical walkthrough on exporting telemetry from tools like VS Code Copilot and using the Aspire Dashboard to visualize traces, metrics, and chat-style conversations.
This article details building end-to-end observability for LLM applications using FastAPI and OpenTelemetry. It emphasizes a code-first approach, manually designing traces, spans, and semantic attributes to capture the full lifecycle of LLM-powered requests. The guide advocates for a structured approach to tracing RAG workflows, focusing on clear span boundaries, safe metadata capture (hashing prompts/responses), token usage tracking, and integration with observability backends like Jaeger, Grafana Tempo, or specialized LLM platforms. It highlights the importance of understanding LLM behavior beyond traditional infrastructure metrics.
Articles on logging, tracing, and observability including Echopraxia, Blindsight, structured log analysis, and more.
OpenInference is a set of conventions and plugins that complements OpenTelemetry to enable tracing of AI applications, with native support from arize-phoenix and compatibility with other OpenTelemetry-compatible backends.
Trace2 is an open source performance logging/tracing framework built into Git that emits messages at key points in each command, such as process exit and expensive loops.
