AI Validation Before Production Release

Before you ship an AI system to real users, you should validate it across accuracy, safety, bias, performance, and business fit. This is the last step before production—and the cheapest place to catch critical issues.

A typical validation checklist includes:

• Offline test set evaluation (accuracy, coverage).
• Safety and abuse testing (prompt attacks, harmful outputs).
• Bias and fairness analysis on key cohorts.
• Latency, throughput, and cost benchmarks.
• Stakeholder review and sign‑off.

For context on how this connects to failure modes and ongoing monitoring, see the AI Quality Assurance pillar page.