system design

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

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 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.

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 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.

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.

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.

Define what unread means, preserve edits and permissions, and make every action item traceable. The design must tell users when the summary covers only part of their inbox.