Traditional APM tells you the server is up. It will not tell you why answers got worse on Tuesday. AI observability traces retrieval through generation, ties outputs to user feedback, and makes prompt changes auditable — infrastructure you want before ten design partners, not after a viral launch. Without traces, debugging a bad answer means reproducing user luck — with traces, you replay retrieval IDs and prompt versions in minutes.
What to instrument
Every request should log: request ID, tenant, model, prompt version, retrieved document IDs, token counts, latency, and user feedback. Hash prompts when full content logging is prohibited by policy. Structured logs beat grep through unstructured stack traces when debugging faithfulness regressions. Include tool call latency separately from LLM latency — slow CRM lookups masquerade as model slowness in aggregate dashboards. Attach user feedback events to trace IDs within five seconds of submission so PMs correlate quality and sentiment.
Separate metrics for retrieval, generation, and tool calls — bottlenecks hide in different layers. Export the same trace ID to frontend analytics so product can replay bad sessions. Sample full prompts in staging only; production should default to hashes unless compliance requires verbatim audit with retention limits. Export traces to your existing log aggregator if the team already lives in Datadog or Grafana.
Tooling choices
LangSmith, Langfuse, Helicone, and OpenTelemetry-compatible backends all work. Pick before launch — migrating traces loses historical baselines. Align tooling with your framework: LangChain-native vs provider-agnostic. LangSmith fits LangChain-heavy stacks; Langfuse offers open-source self-host for data residency; Helicone focuses on cost attribution. Choose before launch — migrating loses baseline comparisons needed for regression detection.
Start with one dashboard: p95 latency, cost per task, faithfulness score on golden set, feedback rate. Expand when those four metrics are green consistently. Observability tools fail when nobody looks at dashboards weekly. One dashboard with four metrics beats twelve dashboards nobody opens: p95 latency, cost per successful task, golden-set faithfulness, weekly feedback volume. Assign a weekly trace review owner in the engineering rotation.
Alerts and response
Alert on faithfulness drops beyond threshold, cost spikes per user, and tool-call error rates. Tail latency drives chat churn — mean latency hides painful p99 experiences. Alert when faithfulness drops more than five points week-over-week — gradual drift evades absolute thresholds. Page on provider error rate spikes; email on cost-per-user anomalies that suggest abuse or misconfigured loops.
Pair automated alerts with a weekly human review sample. Runbooks for model outage and index corruption should exist before production traffic. See our engineering culture post on observability in AI-native software for deeper patterns. Runbooks should cover: empty retrieval, index corruption, model 429 storms, and toxic output spikes. Pair with our AI-native observability post for org-wide adoption patterns beyond engineering dashboards.
What to do next
Start with one measurable outcome — latency, accuracy, adoption, or revenue — and design your stack around that constraint. GenOrbit ships production AI systems across healthcare, astrology, fintech, and SaaS; if you want a second pair of eyes on architecture or delivery, we are happy to talk.
Related: /services/llm-rag-agent-development · /blog/observability-ai-native-software · /blog/evaluating-llm-output-ragas-trulens