When hiring engineers who list AI on their resume, the most revealing question is whether they used evals to measure improvements. Building AI-powered features means working with stochastic systems, so you need a structured way to know if version 2 performs better than version 1. The approach: build a dataset from real user behavior, create a test suite to run against different models and prompts, maintain a human-in-the-loop fallback, and continuously feed failures back into the dataset. This eval discipline is the real competitive moat — everyone has access to the same models, but your proprietary dataset and domain expertise are what differentiate your product.
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