Qlarify Labs
Evals
How we evaluate AI systems to find their limits. Each entry is a repeatable testing technique — the durable knowledge, independent of any one model or version.
Qlarify Labs
How we evaluate AI systems to find their limits. Each entry is a repeatable testing technique — the durable knowledge, independent of any one model or version.
5 methods
Serve two variants — prompts, models, or settings — to comparable slices of real traffic and let live outcomes decide which behaves better.
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.
Deliberately inject failures — tool timeouts, malformed tool responses, truncated context, adversarial inputs — to test whether the system degrades gracefully and recovers.
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.
Instrument the whole request — prompt, retrieval, model call, tool calls, tokens, latency, outcome — so real production behaviour becomes evidence you can query, sample, and score.