cost

A strategic decision with a clear default and specific reasons to deviate. The interviewer wants a framework grounded in evals, volume, constraints and team capacity.

The first vocabulary word of AI product management. Tokens are the unit of cost, speed and memory, and a PM who can't reason in them can't price, budget or design an AI feature.

A larger context window changes what fits in a request. Decide whether to use it by checking corpus size, freshness, permissions and cost per successful query, including cache misses.

Fine-tuning is sometimes the best decision a team makes and often a costly detour. The interviewer wants the test that tells them apart, with numbers.

A puzzle with a precise answer. Price per token is only comparable if a token means the same thing, and across model generations it often does not.

A design trade-off with cost, accuracy and latency all pulling differently. The interviewer wants a measured comparison on real receipts and a hybrid that sends each image down the cheapest path that works.

A question about fairness and unit economics hiding in a tokenizer. The interviewer wants the mechanism, the arithmetic, and options that do not leave non-English users with a worse product.

Distillation is how teams get large-model quality at small-model cost on a specific job. The interviewer wants to know when that trade is worth the engineering, and what can go wrong.

A quality problem with a technical cause and a product solution. The strong answer explains why long contexts degrade and designs conversation management rather than hoping a bigger window fixes it.

Explain what the model sees on each call, why more context can make answers worse and costlier, and which parts of the context recipe a PM owns.

Output limits, reasoning that eats the budget and models trained on shorter answers all cut reports short. Generate by section, check why each response stopped and count failures as cost.

A full-stack AI system design with scale, money, languages and angry customers. The interviewer wants tiers: cheap triage for everything, an agent where it pays, and humans where it matters, with the cost per day worked out.

A short question with a large payoff. A PM who knows the one rule of prompt caching can cut a feature's input bill dramatically without touching quality.

Latency-dominated system design. A suggestion that arrives after the developer has typed the next characters is worthless, so every design choice is about milliseconds, context and cost per keystroke.

Generation at scale where the model writes copy nobody reviews before it reaches millions of phones. The design is about templates, guardrails and cost, more than about the model.

A cost lever that can silently serve wrong answers and leak one user's data to another. The interviewer wants the failure modes and a design that keeps the savings where they are safe.

A runaway agent is a cost problem, a reliability problem and a product problem at once. The answer is part diagnosis, part guardrails, and part design for what happens when a budget runs out.

Progressive constraints test whether you can adapt under pressure without breaking what works. Principal-level candidates handle each constraint with the right mechanism, verify each change, and explain trade-offs.