You deploy an LLM-powered feature and your PM asks: "How good is it?" You pause. With traditional software, you run tests, inputs produce expected outputs, pass or fail. But ask an LLM to summarize an article and it generates a different summary every time. Each version might be perfectly valid. Or subtly wrong. Or great in tone but factually incorrect. How do you measure quality when there's no single right answer? This is the central challenge of LLM evaluation. Teams that skip it ship unreliable products. Teams that get it right build AI features users actually trust. The difference comes down to designing evaluation frameworks that capture what "good" really means for your specific use case.