Most conversational AI evaluation frameworks were inherited from question-answering benchmarks — did the model produce the correct answer given a prompt. That metric is largely meaningless for a multi-turn system where correctness depends on context, tone, and what the user actually needed.
The metrics that surface real problems
Turn abandonment rate — how often users stop mid-session — correlates more strongly with system quality than response accuracy in user studies. A system that gives technically correct answers in a confusing or mismatched tone produces higher abandonment than one that is occasionally imprecise but reads naturally.
How evaluation shifted toward behavioral testing
Since 2023, teams building production conversational systems have moved toward adversarial scenario libraries — collections of edge-case inputs designed to expose failure modes rather than confirm baseline performance. These include topic pivots, emotionally loaded phrasing, contradictory follow-up questions, and deliberate vagueness.
Automated LLM-as-judge evaluation also entered common use during this period. A second model scores responses against rubrics defined by the builder. Anthropic and other labs published guidance on prompt designs for this approach. The technique is imperfect and requires calibration, but it scales in ways human review alone cannot.
The question is not whether the system can answer correctly. It is whether a real user will keep talking to it.