Lesson 1 of 6
Why evals are the real moat
7 min read
You ship a slick demo. It wowed the room. Two weeks later a customer hits an input you never tried, and the thing confidently falls apart. Who was going to catch that?
The model isn't your moat
Your competitor calls the same API, with the same weights, for the same price. The model is a commodity. What they can't clone is your evals — a repeatable way to measure whether your system does your job, on your data. That measurement is the moat, and it compounds every time you add a case.
An eval is a fixed set of test cases you run your system against to get a score — turning 'looks good' into a number you can defend and track.
'Looks good to me' doesn't scale
Eyeballing a few outputs — vibes — works for the first demo and nothing after. You can't eyeball a thousand requests, you forget last week's edge cases, and a confident answer reads the same whether it's right or a hallucination. Evals are how you check at scale instead of praying.
A great demo is a draft, not evidence. One eval run beats a hundred confident-looking outputs.
Start absurdly small. Even ten hand-labeled examples in a spreadsheet beats zero — you grow the set as real failures teach you new cases.
The shape of it
- —The model is a commodity; your evals are what competitors can't copy.
- —Vibes catch the first demo and nothing after — measurement scales, eyeballing doesn't.
- —A confident output is a draft until an eval says otherwise.
A teammate wants to ship a feature because 'the demo looked great.' What's the eval-minded response?
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