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Engineering
Evaluation Is the Feature
If you can't measure whether last week's change made the system worse, you don't have a product — you have a demo with good luck.
What we've learned putting AI systems in front of real users — including the parts that didn't go to plan.
The model is rarely the problem. It's the six unglamorous things nobody scoped: data access, ownership, evaluation, monitoring, training and the decision about what stays human.
If you can't measure whether last week's change made the system worse, you don't have a product — you have a demo with good luck.
A simple scoring exercise for finding the tasks where AI actually pays off — and the ones that just look automatable from a distance.
Full automation is rarely the right target. Deciding the review points early is what keeps a system defensible later.
We optimised for accuracy and shipped something the planning team didn't trust. What we changed, and what we'd do differently.
Per-feature cost monitoring, caching and routing between models — the practices that stop a launch from becoming a budget problem.
Three questions that usually settle it, and why "wait" is a legitimate answer more often than vendors admit.
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