The A/B Test That Exposed Our AI Hallucinations—and How We Fixed It Without Slowing the Ship
A production-first blueprint for running safe AI AB experiments with instrumentation, guardrails, and measurable risk controls.
Your observability stack wont save you if your AI hallucinates in prod—time to bake guardrails into AB tests now.Back to all posts
In this article we explore a production first approach to AB testing AI models, not in a lab, but where real users and real data collide. We start with instrumentation that makes a single misbehaving variant visible within minutes, not hours or days. Then we embed safety guardrails that force a safe outcome if drift or
hallucination spikes appear during rollout, so you can stop or roll forward with confidence. The result is a testing discipline that yields measurable risk reduction and faster learning at scale.
Rather than chasing vanity metrics, you measure outputs that matter to customers and business risk. You harden the pipeline with policy as code, robust observability, and a clearly defined rollback path. This is how you turn AI experiments into provable, safe product features.
The technique blends four elements I’ve seen work across fintech, retail, and travel tech: precise instrumentation, drift aware evaluation, guardrail governed deployments, and a disciplined incident lifecycle that closes the loop back into modernization workstreams.
If your current AB tests look good on paper but fail in prod, you’re not alone. The real value comes from a test harness that travels with production load, captures context, and makes risk decisions automatically.
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Key takeaways
- Instrument at the per-request level to tie outputs to context and model version.
- Define drift, hallucination, and latency SLOs that drive guardrails.
- Use canary style AB tests with policy as code to block unsafe progress.
- Center observability on input context, variant, and output to enable fast triage.
- Treat postmortems as modernization backlogs for rapid remediation.
Implementation checklist
- Define per-request telemetry fields: variant_id, model_version, input_hash, latency_ms, output_label, confidence; route to OTLP and Prometheus.
- Establish SLOs for P95 latency and hallucination rate per AI variant; set alert thresholds.
- Implement cohort based traffic routing and hidden evaluators to measure real world impact without affecting all users.
- Deploy data drift detectors (KL divergence, feature distribution shifts) with automated gating via OPA policies.
- Enable canary rollouts with Argo Rollouts and feature flags; require auto kill switches if risk thresholds are breached.
- Establish runbooks for rapid rollback, automated P99 latency checks, and blameless postmortems linked to modernization backlog.
Questions we hear from teams
- How do we prevent drift from invalidating AB tests in production AI?
- Define cohort based evaluation, drift detection, and automated rollouts with guardrails tied to SLOs.
- What metrics matter most for AI AB tests in production?
- Latency tail, hallucination rate, drift score, and user impact calibrated across variants.
- Can you integrate with our existing observability stack?
- Yes we design AB tests around OpenTelemetry Prometheus and Argo Rollouts to connect to your dashboards.
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