Which considerations are uniquely important for A/B testing LLM systems vs standard UI testing?

**A/B testing for LLMs** splits live traffic between a current system (control) and a candidate change (challenger), then compares outcomes. The mechanics look like any web A/B test, but four things are genuinely different, and the question asks you to separate the real differences from the things that apply everywhere. The first real difference: LLM quality metrics are indirect. A button click is a clean binary, but helpfulness and faithfulness are fuzzy proxies you have to operationalise with judges or surveys. They are noisier and harder to define. The second: multi-turn conversations carry over. If you flip a user between control and challenger mid-conversation, the earlier turns contaminate the later ones. So you assign by user (or session), not by request. The third: that higher variance means you need a larger sample and a larger minimum detectable effect to reach significance, so tests run longer. The fourth: a bad LLM output can harm a user immediately, so you add guardrail metrics that auto-stop the challenger if safety or quality collapses. The two wrong options, the rigid 7-day rule and the idea that a bare p-value below 0.05 is enough, are just standard A/B-test practice. They apply equally to UI tests and are not specific to language models.

**A/B testing for LLMs** keeps the same statistical skeleton as web experimentation but changes four design choices. The question correctly flags A, B, C, and E. Quality metrics (A) are indirect. Clicks are clean; helpfulness and faithfulness must be operationalised through judges or thumbs signals. They are noisier and contested. Multi-turn carryover (B) forces user-level or session-level assignment. Switch a user mid-conversation and earlier turns pollute later ones. Request-level randomisation breaks the analysis. Higher signal variance (C) raises the minimum detectable effect and the sample size you need for power. Fuzzy quality metrics carry more noise than a click rate, so tests run longer to reach significance. Catastrophic guardrails (E) auto-stop the challenger if safety or quality drops sharply. A bad output can harm a user instantly, unlike a worse button color. The two distractors are not LLM-specific. The 7-day rule (D) is generic advice to capture day of week effects; it applies equally to any test. And a bare p-value below 0.05 (F) is never automatically sufficient. Multiple metrics demand correction in any A/B context. Both are standard practice dressed up as LLM-unique.