metrics

A strategy-sized design question. The tempting answer replaces the search bar with a chatbot. The scoring answer keeps fast keyword search for the shoppers who know what they want and uses AI for the shoppers who don't.

Trust is easy to survey and hard to measure. The interviewer wants a composite built from behaviour, interpreted carefully, and validated against outcomes the business cares about.

Search visits with an AI summary produced 40% fewer outbound clicks in one large study. Decide which questions deserve a complete answer, which deserve a doorway and how you will measure the difference.

A diagnosis question with five plausible causes. Strong candidates rank them by likelihood and name the cheapest check for each instead of reciting all five.

'Hallucination rate' sounds like one number. In practice it is three different failures, and the one that matters most is the hardest to see.

A classic experiment-design question with AI-specific traps: novelty that fades, quality that differs by segment, and a cost line that classic A/B tests never had.

A vocabulary question with a sharp edge. The cheapest model per token is regularly the most expensive per finished job.

Usage counts are the trap. The answer that scores connects quality, behaviour, cost and risk to one business outcome, and says what evidence would change the primary metric.

A vocabulary question about speed. The right metric depends on whether the user is watching text stream in or waiting for a finished result.

More AI-generated code can mean more code to review and maintain. Choose a metric that moves when developers deliver useful, reliable work.

AI features are unusually good at producing engagement that is actually friction. The question tests whether you can tell the two apart in the data.

The most expensive AI failures are the confident wrong answers that users accept. This question tests whether you can find their footprints in behaviour data.

A fundamentals question. The AI twist is that the fastest leading indicators come from what users do with the output in the first few seconds.

One person's AI output becomes another person's input. Choose the experiment unit, account for shared work and decide what a large user count can actually prove.

Rank surviving signals by what they actually prove, decide when to pause exposure and reconcile missing events without inventing outcomes.

A model release and a campaign happened together. Reconstruct the success rate, explain the mix shift and decide what evidence should drive the next change.

A platform metrics question. Token volume is the obvious answer and the incomplete one. The interviewer wants the developer funnel, reliability, economics and migration health in one coherent set.

Every PM has trusted a number they shouldn't have. Panels want to hear a real mistake, how it was caught, what it cost, and the habit that changed.