Overview
Leaders ask a simple question: is this AI good enough to ship? Vague answers like “it feels better” do not survive audits, budget reviews, or customer escalations.
Introduction
An evaluation framework needs offline tests, online metrics, and human review—aligned to the workflow you are automating, not generic academic benchmarks alone.
Offline suites
Maintain golden datasets with expected answers, rubrics for tone and policy, and adversarial cases (jailbreaks, PII traps, edge locales). Score automatically where possible; sample human review for high-risk categories.
Online signals
Track task completion, escalation rate, thumbs-down reasons, latency, and token cost. Segment by user cohort and document type so regressions do not hide in averages.
Change management
Every prompt, model, or retrieval change ships behind a version ID and must pass the suite. Canary releases with automatic rollback beat big-bang swaps.
Governance reporting
Translate metrics into risk language for security and compliance: hallucination rate on regulated topics, PII leakage attempts blocked, and model vendor SLAs.
Key Takeaways
If you cannot measure quality, you cannot manage it. Evaluation is the operating system of enterprise AI—not a pre-launch checkbox.