10How do you evaluate an LLM, and why are benchmarks and LLM-as-judge both unreliable?▼hard★ EssentialOpenAIAnthropicGoogle2 repliesunlockedEvaluation is the hardest, most underrated part of shipping LLMs. The signal is knowing why public benchmarks mislead, why LLM-as-judge is biased, and how to build a task-specific eval you actually trust.Open full answer →
71What's the difference between evaluating a model and evaluating the product around it, and why do you need both?▼mediumOpenAIAnthropicScale AI2 replies◆ premiumA model that scores 92% on your eval can still ship a product users hate, because the model is one component in a system. Teams that only do model evals get blindsided. Here is the distinction that matters.Open full answer →
03Design an evaluation and guardrail stack for an LLM feature: jailbreaks, toxicity, and hallucination.▼hard★ EssentialAnthropicOpenAIGoogle2 repliesunlockedShipping an LLM feature safely is an evaluation problem before it is a model problem. The signal is a layered eval-plus-guardrail design with honest, segmented metrics, not a single 'safety classifier.' Here is how to measure and defend each failure mode.Open full answer →
