A Java engineer reaches for a search index plus a service. RAG adds two twists: the index is vectors, so memory is the sizing question, and the answer must never cite a chunk the asker is not allowed to read — access control is part of retrieval, not a filter bolted on afterwards.
45 min round9 computed numbers4-part eval plan8-point rubric
⚠️ Planning numbers, not measurements. Hardware and model facts
are dated on the numbers sheet; traffic, prices and efficiency are labelled
assumptions. Replace them with your own measurements (the vLLM load-test exercise) before quoting them.
The prompt
Design question-answering over a 50,000-person company's documents, with per-user access control.
Attempt it first: the 45-minute round
Set the timer, answer out loud or on paper, then score yourself against the rubric before you read the model answer below.
Framing (5 min) — Ask questions, fix the scale and the latency/quality/cost targets, state assumptions with numbers.
Architecture (10 min) — Draw the boxes end to end: data in, the model call, storage, serving, feedback.
Deep dive (15 min) — Pick the hardest part and do the arithmetic: tokens/s, KV memory, QPS → replicas, cost per 1,000 requests.
Trade-offs (10 min) — Name what you would trade (batch vs latency, quality vs cost, build vs buy) and what breaks.
Wrap-up (5 min) — Evaluation plan, monitoring, failure modes, and what you would do next.
45:00
Not started
Self-grade
Tick what you did. 0 of 8.
The model answer, step by step
Five steps, one tap each. The readout gives the step's answer and lists the numbers it uses; the same numbers are highlighted in the table underneath.
Enterprise search / RAG at scale, one tap per step
👉 Predict first, then tap. Every number below is computed from the assumptions in the table.