Production AI / 1 min read
Production AI needs reviewable evidence.
The reviewer needs sources, checks, and unresolved issues that support an acceptance decision.
Design evidence around the result
An AI-generated operational summary may sound complete while relying on stale data or an unresolved exception. A long execution log does little to help a reviewer identify those conditions. The evidence should explain the source period, material assumptions, checks performed, and remaining uncertainty.
A practical starting point is one representative output and the decision its reviewer must make. The team can then define which evidence permits acceptance, which discrepancy requires correction, and which missing input prevents a decision.
Measure review effort as well as model behavior
A Production AI Lighthouse can include this evidence package in its acceptance criteria. Review time, correction effort, and successful outcomes belong alongside quality, latency, and operating cost. A faster draft only creates value when the complete review path produces a usable result.
A relevant next step
Production AI Lighthouse
Tera Earlywine helps a business owner and data sponsor bring one defined AI workflow into production. Scope includes evaluation, permitted actions, human escalation, monitoring, and operational handoff, with acceptance criteria for quality, latency, cost, and successful outcomes.
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