When Your AI Model Hallucinates in Production—and Your Guards Are Sleeping
A field-tested blueprint for an evaluation harness that gates generative features before, during, and after release.
Your AI feature will hallucinate in production—it's the guardrails that save the day.Back to all posts
On a Friday release our AI assistant hallucinated a policy update and began approving refunds for non-existent orders, triggering a flood of support tickets and churn. The incident revealed how quickly a model can cause business harm when observability stalls at the API boundary.
To fix it we built an evaluation harness that runs in parallel to production, receiving the same inputs via shadow traffic, evaluating outputs against guardrails, and surfacing risk signals in a dedicated dashboard.
During rollout we gated the feature with a fail-safe policy, enabling rapid rollback if the hallucination score crosses a threshold; we wired traces across services with OpenTelemetry and Prometheus so you can see exactly where risk arises.
Post-release we established a cadence of automated re-evaluation, drift detection, and a prioritized modernization backlog tied to product KPIs like CSAT and MTTR, turning incidents into measurable improvements.
Code/Config Snippet (one-liner): opa policy: package ai.guardrails; default allow = false; deny[reason] { input.hallucination > 0.1; reason = \"hallucination-detected\" }
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Key takeaways
- Define AI SLIs around hallucination rate, drift score, latency budgets, and user impact.
- Run shadow prompts and synthetic workloads in pre-release to quantify risk.
- Use policy-as-code (OPA) and guardrail circuits to fail safe.
- Create a closed-loop feedback line from post-release evaluation to governance and remediation.
Implementation checklist
- Define AI SLIs with measurable thresholds (hallucination rate, drift, latency).
- Design an evaluation harness that runs on pre-prod with shadow traffic and synthetic prompts.
- Implement policy-as-code guardrails (OPA) to gate outputs in real-time.
- Instrument end-to-end traces with OpenTelemetry and Prometheus across AI-enabled flows.
- Establish a fail-safe rollback and auto-remediation workflow with canary deployments.
- Institute a weekly drift review and post-release remediation backlog linked to your modernization plan.
Questions we hear from teams
- What is an evaluation harness for AI, and why do we need it?
- It’s a combined testbed and runtime guardrail that evaluates AI outputs against safety criteria before, during, and after release, using shadow traffic, drift detection, and policy checks.
- What metrics matter for live AI outputs?
- Factuality or hallucination rate, drift score, latency and throughput budgets, and user-impact metrics like CSAT or support load, all tied to a governance-facing dashboard.
- How often should we run post-release evaluations?
- Weekly automated drift checks with a monthly governance review; tie findings to a modernization backlog and product KPIs to close the loop.
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