The model provider just updated the model behind the API endpoint. Describe the regression testing process before allowing traffic.

Provider model updates are **silent regressions** until you prove otherwise. The provider optimized for their benchmarks, not your specific use case. Here is the process to protect your users. First, run your golden set. This is a curated collection of test cases with known good outputs covering your critical query types, edge cases, and known past failures. Score the new model on the same metrics you track in production: accuracy, faithfulness, format compliance. Second, compare against your baseline. The baseline is the scores from the previous model version. Any significant drop in any metric is a flag. Break results down by query type and user segment so a slice level regression does not hide behind a good aggregate number. Third, run your safety subset. Prompt injection attempts, jailbreak patterns, content policy tests. A quality improvement can come with a safety regression that your quality metrics will not catch. Fourth, check latency and cost. The new model might be slower or more expensive. Measure p50 and p95 latency plus cost per request. Finally, canary the rollout. Route 5 percent of traffic to the new version and monitor for 24 to 48 hours. If metrics hold, expand gradually. If anything regresses, roll back automatically.

The core principle: **provider model updates are silent regressions** unless you actively test. The provider does not know your edge cases or SLAs. The process has six steps. Run your golden set against the new version, covering all critical query types, languages, and known failure modes. Compare scores against the last known good baseline using a paired statistical test; set a regression threshold (typically 2 to 5 percent for quality). Run slice level analysis so a regression in one query type does not hide behind aggregate improvement. Run a dedicated safety and red team subset: prompt injection, jailbreak, PII leak probes. Safety regressions are a hard block regardless of quality improvement. Check latency (p50, p95, p99) and cost per token; a quality improvement that doubles latency may not be viable. Canary rollout: route 5 percent of traffic to the new version, monitor for 24 to 48 hours, expand to 25, 50, then 100 percent. Any metric regression triggers automatic rollback. The total elapsed time from model update to full rollout should be 3 to 5 days for a production system.