How we evaluate LLMs before they touch a customer
Offline benchmarks lie. We share the eval harness, golden datasets, and human review loops we trust in the real world.
- 01Public benchmarks rarely predict real-world behaviour.
- 02Build golden datasets from your actual use cases.
- 03Keep humans in the loop for high-stakes evaluation.
Public benchmarks make for great headlines and poor decisions. A model that tops a leaderboard can still fail your users in ways no generic test would catch.
Build your own golden set
We assemble evaluation data from your actual use cases — real questions, real edge cases, real failure modes — and grade against it. That's the only benchmark that predicts production behaviour.
Keep humans in the loop
Automated scoring scales; human judgement anchors. For anything high-stakes, we pair automated evals with structured human review before a model touches a customer.
Offline benchmarks tell you a model is capable. Your own evals tell you it's ready.
And because models, prompts, and data all drift, evaluation is continuous — a regression suite that runs on every change, not a one-time gate.
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