engineering · 1 min read
Distributed Tracing for LLM Workloads
A complete LLM trace covers prompt, retrieval, tool use and post-processing. Here is how Bhogar AI builds those traces and what to do with them.
BABhogar AI TeamProduct & Engineering
A complete LLM trace tells the story of one request: which prompt fired, what was retrieved, which tools were called, what the model returned and how long each step took.
Why it matters
OpenTelemetry semantic conventions for LLMs are now stable. Adopting them gives you portability across observability vendors and a common language with the wider ecosystem.
How Bhogar AI approaches it
Bhogar AI emits OTel-compliant traces with the GenAI semantic conventions out of the box. Trace data flows to the built-in viewer and to any OTLP-compatible backend.
- OTel GenAI semantic conventions
- Built-in trace viewer with timeline
- OTLP export to Datadog, Honeycomb, Grafana, New Relic
- Per-span cost and token telemetry
- Always-keep on errors and slow runs
What you get
Engineers debug AI incidents in minutes with traces instead of hours with logs.