Notion AI System Design interview questions

A RAG pipeline is the obvious first sketch. A strong answer starts with permissions instead, because one leaked salary document ends the product.

A pipeline design question with an automation trap. Writing summaries is the easy part; writing into the system of record that forecasts revenue is where the design needs care.

Every AI product depends on services it does not control. The interviewer wants a degradation plan by feature, written and tested before the outage starts.

A deadline you did not choose and a change that touches every output. The interviewer wants a plan that treats API compatibility and behavioural compatibility as different problems.

A design question with a constraint dropped halfway. The interviewer wants to see residency flow through every component: model calls, logs, caches, embeddings, evals and support access.

A security design question that decides whether enterprise customers will sign. The answer has to make cross-tenant leakage impossible by construction, and then prove it with tests.

A small architectural component with outsized strategic value. The interviewer wants to hear what it does day to day and why it matters when a provider changes prices or goes down.

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.

A large-scale design question where every error is someone's real harm: harmful content left up, or legitimate speech taken down. Strong answers tier by severity and confidence, keep humans in the hard cases, and measure both kinds of error per language.

Without traces, every quality complaint is a guess. The interviewer wants the fields that make debugging, evaluation and cost control possible, and an awareness of what should not be stored.

Search plus generation at consumer scale. The design has to deliver fresh, cited answers in a couple of seconds, resist manipulated pages, and keep cost per query viable.

Launches fail on rate limits more often than on model quality. This question checks whether you can turn expected usage into tokens per minute, compare it with limits, and plan headroom.

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.

Translation looks solved until a price, a date or a promise is mistranslated between two strangers doing business. The design centres on speed, protecting critical details, and letting both sides see the original.

A regulated pipeline where the model reads documents but must not be the one deciding who gets an account. The interviewer wants extraction, checks against authoritative sources, fraud signals, human review and data minimisation.

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 principal-level platform question. Each team building its own agent stack means dozens of security models, cost blind spots and incompatible tools. The platform has to make the safe path the easy path without slowing teams down.

A platform design question about the tooling that decides launches. The hard part is not running prompts at scale; it is making error analysis, labelling and judge validation easy enough that teams actually do them.

At a billion requests a day, a fraction of a cent per call is millions of dollars a month and every millisecond is capacity. Principal answers start from the arithmetic and reshape the feature, the models and the serving to fit.