135When should a request hit a reasoning model, and how do you stop it from overthinking?▼hardNewOpenAIAnthropicGoogle◆ premiumMost candidates answer 'use the reasoning model for hard problems' and stop. The interviewer wants it framed as an eval and a budget problem: how you prove the extra thinking tokens paid off, and what you do when the model talks itself out of a correct answer.Open full answer →
95Design an A/B testing platform for LLM features (prompts, models, retrieval) with trustworthy metrics.▼hardNewOpenAIGoogleMicrosoft2 replies◆ premiumRunning experiments on LLM features is tough because outputs are open-ended and quality is fuzzy. See how to assign traffic, choose metrics beyond engagement, tame variance from non-determinism, and dodge the traps that let a winning variant lose once it ships.Open full answer →
