Most AI products ship with incomplete test coverage because traditional QA practices do not map neatly to stochastic outputs and fast-moving models. An evaluation sandbox gives teams a controlled environment to probe behaviours, compare model changes and capture regressions before production users experience them.
Good sandboxes look like production: real prompts and datasets, realistic safety and latency budgets, and the same observability stack you run in the live path. They also add synthetic stressors such as adversarial prompts, long-context inputs and rate spikes so teams understand where the system bends and where it breaks.
An evaluation harness should blend automated tests (static checks, policy filters, groundedness scoring) with structured human review. Practitioners typically combine precision/recall on labelled tasks with rubric-based scoring for tone, actionability and risk. The goal is not a single metric, but a scoreboard that makes trade-offs explicit.
Versioning matters. Lock datasets, prompts and policies alongside model versions so you can reproduce findings, bisect regressions and run A/B comparisons quickly. Treat prompt and tool changes as first-class releases, not incidental tweaks.
Gatekeeping is a product decision, not just an engineering one. Define criteria for when a model update can graduate from sandbox to canary to full rollout, and who can approve exceptions when business urgency conflicts with model risk.
Finally, keep learning loops tight. Capture production drift signals (new intents, novel data, safety incidents) and feed them back into the sandbox. The best teams operate the sandbox as an ongoing program, not a pre-launch checklist, so the quality of AI systems improves with every release.