Hamel Husain explains why AI teams can't rely on informal vibe checks to judge model outputs as projects scale. Instead, they need systematic evaluation frameworks that track performance consistently, catch regressions, and let teams make data-driven decisions about model development.
Most people building AI products use vibe checks, which means just check and see whether or not what you're getting makes any sense. And that's a great way to start, but it doesn't scale. What you want to do is have systematic evaluations or measurements that can tell you whether or not what you're doing is making consistent progress. And there are ways to do that, even despite the fact that your outputs are stochastic and change and are subjective. And that's what evals let you conquer.
