102You set temperature to 0 and send the same prompt twice, and the outputs differ. Why, and when does it matter?▼hardAnthropicOpenAIDatabricks◆ premiumTemperature 0 is not the same as deterministic, and the reason lives in the GPU kernels, not the sampler. What gets scored is naming the batch-invariance problem and knowing which fixes are real versus placebo.Open full answer →
56You need to move a live feature to a different model or provider. How do you cut over without a quality regression?▼hardOpenAIAnthropicDatabricks◆ premiumA provider swap does not fail with a 500. It fails by staying up and quietly getting worse. The signal is knowing what actually changes under you on a model swap, and the shadow-plus-staged-ramp protocol that catches it before your users do.Open full answer →
58Your LLM app aces the eval set and is brittle in the wild. How do you test robustness to input variation?▼mediumOpenAIAnthropicScale AI◆ premiumYour eval set is clean, well-punctuated, and phrased the way you think about the problem. Your users are not. The move that turns this from a vibe check into an engineering artifact is measuring invariance rather than accuracy, and gating on the number it produces.Open full answer →
57What is AI Engineering, and how is it different from Machine Learning Engineering?▼easyOpenAIAnthropicDatabricks◆ premiumThe opener in half of all Applied AI screens, and most candidates answer it with a definition nobody scores. The distinction that actually earns points is about where you start and what your bottleneck turns out to be.Open full answer →
