Why evaluate?
Why AI teams need evaluation discipline — the cost of shipping unmeasured AI.
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Why AI teams need evaluation discipline — the cost of shipping unmeasured AI.
Most teams ship AI by intuition. They pick a model because it's popular. They iterate prompts because the last few examples felt better. They watch CSAT in production because that's the only signal that survives. By the time they know something's wrong, customers have noticed.
Wrong model from the start. A 4-week feature build gets thrown out when the foundation model can't do the task.
Silent regressions. A prompt change five PRs ago broke an edge case nobody noticed; customers find it first.
Vendor migrations driven by hunches. Switching from one provider to another without quantitative justification.
Eval-by-eyeballing. A handful of cherry-picked examples drive ship/no-ship decisions.
Evaluation discipline gets you out of all four traps.
Quantitative model selection. You pick a model because it scored higher on the benchmarks closest to your task. Not because it was on the front page of HN.
Versioned regression suites. Every prompt change runs against a known set with a known baseline.
Continuous scoring on production traffic. When live behavior drifts, you know before customers do.
Auditable evidence. Releases cite the evaluation that gated them.
A shared mental model for evaluation (this documentation)
A catalog to anchor your model choices (Stratix Public)
A workspace to run private evaluations (Stratix Premium)
The "hopeful" launch. Shipping without quantitative evidence the AI works.
The slow regression. Quality erosion over a quarter that nobody catches because nobody's watching the trend.
The vendor lock-in mistake. Picking a model based on superficial features and discovering, six months later, that switching is now expensive.
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