Evaluating LLM Features Before You Ship
Demos prove a feature can work once. Evaluation shows how often it works, for whom, and at what cost.
The gap between a convincing LLM demo and a dependable feature is measurement. Without evaluation, every prompt tweak is a guess and every model upgrade is a risk. The good news is that useful evaluation does not require a research team, only discipline and a representative dataset.
Start with fifty to a hundred real examples that cover common cases, edge cases and inputs the feature should refuse. Label the expected outcome, not the exact wording. For extraction tasks that means the correct fields; for summaries it means facts that must appear and facts that must not.
Combine automatic checks with human review. Code can verify structure, length, forbidden content and factual anchors. People are needed for tone, helpfulness and subtle errors. Sample a fixed percentage of outputs for human review every week so drift is caught early.
Using a model to grade another model works when the rubric is specific. "Rate helpfulness from 1 to 10" produces noise; "Does the answer state the cancellation deadline from the source? yes or no" produces a usable signal. Calibrate the grader against human labels before trusting it.
Track cost and latency next to quality. A change that improves accuracy by two points but doubles response time may not be a win. Store every evaluation run so you can compare versions over time and make model upgrades a routine decision rather than a leap of faith.
