Section 051 · Chapter 8, Operating AI: Observability, Relevance, and Economics
Observability and Tracing for AI Systems
You cannot debug a final answer if you cannot see the path that produced it.
latencyobservabilityretrievalobservability tracing
What to do
- Use a small set that covers user-visible quality, safety, latency, tool reliability, and cost.
- Keep catastrophic events, such as cross-customer data exposure or an unauthorized irreversible action, out of a comforting average.
- Save the user-visible output, prompt assembly, model and prompt versions, policy version, retrieval snapshot, tool inputs and results, timing, permissions, judge decisions, and deployment state.
- Apply privacy and access controls; incident evidence can contain the most sensitive data in the system.
- Do not let uncertainty about the model become an excuse for silence about observed impact.
Evidence to preserve
- Save the user-visible output, prompt assembly, model and prompt versions, policy version, retrieval snapshot, tool inputs and results, timing, permissions, judge decisions, and deployment state.
- Preserve the smallest faithful reproduction, add nearby variants and affected slices, assign an owner, and connect the regression case to the release gate and production monitor.
- Log the request, but also log confirmation.
- Preserve the inputs, versions, configurations, raw outcomes, and results for latency, observability, retrieval, observability tracing needed to reproduce work on Observability and Tracing for AI Systems.
Expert note
In a real release review, traces should have stable correlation IDs, privacy-aware redaction, span-level metadata, model and prompt versions, retrieval snapshots, tool inputs and outputs, token/cost metrics, latency percentiles, judge scores, and links back to eval cases and production incidents.
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Cite this page
Jason Arbon. "Observability and Tracing for AI Systems." Testing AI Knowledge Edition, section 51.
https://jarbon.ai/testing-ai/knowledge/ch051-observability-tracing.html