The AB Test That Stopped Our AI From Hallucinating In Production
A battle-tested blueprint for instrumenting AI in live systems with verifiable guardrails and measurable safety outcomes.
In production, your AI is a moving target—instrument it, verify it, and keep guardrails tight.Back to all posts
In production, your AI is a moving target; you can only trust outputs that you instrument and prove against the business you serve.
We stitched together a practical AB testing framework that measures hallucinations, drift, and latency across prompts, with guardrails that trip before customers suffer.
This is not about dashboards; it’s about operationalizing safety into every deployment, with a living experiment that expands your safe delivery runway.
Our approach centers on coupling product outcomes to model metrics, so when hallucinations spike or drift crosses a threshold, the system can auto-reroute, pause, or escalate to a human-in-the-loop.
GitPlumbers helped us implement this with Argo Rollouts, OpenTelemetry collection, and OPA policies that turn policy into live protections and automated proofs.
Key takeaways
- Instrumentation should be tied to business outcomes, not just telemetry.
- Treat AI outputs as experiments with guardrails and an auditable trail.
- Canary deployments paired with policy-driven gates reduce risk without slowing delivery.
- Guardrails must respond to both model behavior and data drift, not just system metrics.
Implementation checklist
- Define hallucination, drift, and latency SLIs and SLOs for live AI flows.
- Instrument with OpenTelemetry and export to Prometheus; build Grafana dashboards that map to business events.
- Configure AB testing with Argo Rollouts; route 5–20% of traffic to the new variant and monitor guardrails.
- Implement policy-as-code (OPA) to abort unsafe variants automatically when thresholds are breached.
- Establish runbooks for safe rollback, hotfix, and postmortem integration into the modernization backlog.
- Schedule monthly reviews that link incidents to a modernization backlog with owners and deadlines.
Questions we hear from teams
- What exactly is an AI AB test in production?
- It’s running two model variants against live traffic while measuring outputs like hallucination, drift, and latency; decisions are gated by guardrails and business impact is quantified.
- How do you measure hallucinations safely?
- Define a clear hallucination metric, couple it with human-in-the-loop reviews for uncertain prompts, and tie it to a risk-based SLO that triggers guardrail actions.
- How quickly can this framework reduce risk on a live AI rollout?
- Typically 4–6 weeks to instrument, deploy canaries, and mature the guardrails; with more data about high-risk segments, gains come faster as you tighten thresholds.
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