AWS Certified AI Practitioner AIF-C01 Practice Question

Politeness and helpfulness are subjective, qualitative attributes that automated metrics such as BLEU, latency, or token counts cannot reliably capture. Instead, organizations typically rely on human evaluation: reviewers read sample responses and rate them against a rubric that defines desired qualities (for example, politeness levels or usefulness of information). Human judgment can account for nuance, tone, and context in ways automated statistics cannot. BLEU focuses on n-gram overlap with reference text, latency measures performance speed, and token counts track output length-none of which directly indicates whether answers are polite or helpful.