LLM product evaluation

A model can ace academic benchmarks and still make a product users dislike. Product evaluation asks the question that actually matters: does this feature deliver real value to the people using it, end to end? It looks past the model's score in isolation to whether the whole system — prompt, retrieval, guardrails, interface — helps a user accomplish their goal.

Product evaluation blends offline and online signals. Offline, task-specific eval sets measure end-to-end quality on representative inputs, not generic benchmark scores. Online, you watch real behaviour: did the user accept the answer, edit it, retry, or abandon the task; thumbs-up rates, conversion, task completion, and escalation to a human. A/B tests compare versions on these business metrics, and sampled human review reads real conversations for problems no metric captures. The honest measure is downstream outcomes, not the model's standalone cleverness.

Optimising the model in isolation can quietly hurt the product — a more verbose model may score higher on a benchmark yet annoy users, or a confident one may please raters while misleading them. Tying evaluation to genuine user value keeps the team honest about what it is building. It is harder than running a benchmark, because real value is messy and slow to measure, but it is the only score that ultimately decides whether the product succeeds.