Design an A/B testing platform for LLM features (prompts, models, retrieval) with trustworthy metrics.
Running experiments on LLM features is tough because outputs are open-ended and quality is fuzzy. See how to assign traffic, choose metrics beyond engagement, tame variance from non-determinism, and dodge the traps that let a winning variant lose once it ships.
Updated Sep 2026 · Grounded in real GenAI, LLM, and AI/ML engineering interview loops and written to a senior-engineer editorial bar.
Running experiments on LLM features is tough because outputs are open-ended and quality is fuzzy. See how to assign traffic, choose metrics beyond engagement, tame variance from non-determinism, and dodge the traps that let a winning variant lose once it ships.
Lead with where the obvious approach breaks, because that is the judgment they are screening for — most candidates jump straight to the happy path and lose the room.
Then walk the failure back through the pipeline in order, naming the one metric the customer's exec sponsor actually cares about before you propose the fix.
