Practice · Established · Intermediate

AI Observability

Instrumenting AI systems so you can see what was asked, what was retrieved, what the model returned, and what it cost.

What AI Observability is

Observability for AI adds prompt and response traces, retrieval context, tool calls, token counts, latency breakdowns and user feedback to standard service telemetry.

How it works

Every request emits a trace linking user input, system prompt version, retrieved chunks, tool invocations and output, with sampling and redaction for privacy. Dashboards track quality signals alongside cost.

Why it matters

Without traces, debugging a bad answer is guesswork, and cost overruns are discovered on the invoice.

Common uses

→Debugging bad answers
→Cost attribution per feature
→Quality trend monitoring
→Incident review

Strengths

✓Fast root-cause analysis
✓Evidence for evaluation sets

Watch for

✓Logging prompts raises privacy obligations
✓Storage cost at volume

Continue exploring

LLMOps (2)LLM Evaluation Model Drift Data Privacy in AI