Evaluating AI and LLM Features
Explore how to evaluate AI and large language model features effectively by separating offline quality assessments from online product value tests. Learn to use representative task sets, clear rubrics, and human ratings alongside automated judges to ensure safe and helpful support drafts. Understand how to design validity-checked experiments measuring key metrics like resolution rates and guardrails to diagnose failures and confirm real improvements in customer support outcomes.
An LLM-based support-answer draft can receive high offline quality scores while making agents slower or customers less satisfied. The evaluation problem is therefore not whether a draft sounds good in isolation. It is whether the draft improves support outcomes without creating unacceptable risk.
An interviewer may ask, “Add an LLM-based support-answer draft feature. How do you evaluate it?” A weak answer says, “I would measure whether agents click the draft and use an LLM judge to score quality.” Clicks measure exposure or curiosity, not whether the answer resolved the customer’s issue. A judge score can also reflect the judge’s preferences rather than real support quality.
A stronger answer separates two decisions. Offline evaluation decides whether a candidate prompt or model version is safe and useful enough to test. Online evaluation decides whether the feature creates product value for eligible support conversations. Both decisions need explicit guardrails, such as harmful suggestions, increased handle time, higher escalation rate, or a higher complaint rate.
A useful answer sounds like this:
“I would use representative tickets and human ratings to compare draft quality offline, then run a validity-checked experiment on eligible conversations to measure resolution while monitoring safety and operational guardrails.”
A follow-up may ask why offline quality is insufficient. The check is concrete. Compare drafts on the same support tickets, then inspect whether agents using the drafts resolve more cases without increasing handle time or unsafe responses. If offline scores rise while resolution falls, the offline rubric missed a product-relevant failure mode.