Qlarify Labs
Catalog
How to find the limits of AI systems. Each entry is a repeatable testing technique — the durable knowledge, independent of any one model or version.
4 methods
A/B testing in production
Serve two variants — prompts, models, or settings — to comparable slices of real traffic and let live outcomes decide which behaves better.
Canary releases & staged rollout
Route a small slice of real traffic to a new model or prompt first, watch it closely, and widen or roll back based on what the canary shows.
Chaos engineering for AI systems
Deliberately inject failures — tool timeouts, malformed tool responses, truncated context, adversarial inputs — to test whether the system degrades gracefully and recovers.
Drift & decay monitoring
Re-run a fixed suite against each release and over time, watching for the quiet regressions and capability decay that a one-off evaluation can't see.