How do I A/B test LLMs in production safely?

Production LLM A/B testing has four guardrails. (1) Shadow traffic first: send a fraction of production prompts to the candidate model in parallel, compare offline. (2) Hold-out judge: use a third, frozen LLM (or human raters) to score side-by-side without knowing which is candidate. (3) Safety gates: enforce minimum thresholds on refusal rate, JSON validity, and p95 latency — a model that's 2 points better on MMLU but 3x slower at p95 is not a win. (4) Sequential testing: use mSPRT or always-valid p-values so you don't peek and over-call winners on small samples.

What this rarely catches is silent post-rollout drift on the winner. Once gpt-4o-2024-08-06 is your prod model, you still need continuous regression monitoring on that snapshot. ModelWatch fills that gap — daily fixed-suite scores on the model you actually deployed, with alerts when any metric breaks its control chart.