Which LLM evaluation metrics actually help product decisions
I keep coming back to the same problem with model evals: many of them look polished, but they are not very useful once a team has to make an actual product decision. Product teams need something more grounded: sample coverage that matches the real task, failure behavior that can be reviewed by humans, and cost or latency measurements that reflect the full workflow instead of just a single prompt.
I care less about one headline score and more about whether the team can say, with confidence, where the model is safe, where it still needs human review, and how painful a regression would be after a prompt or model swap.
Good evals do not just rank models. They reduce ambiguity in shipping decisions, which is usually the harder problem anyway.