1 min lesson
A forty-minute practice clock
Rebuild the sequence in "A forty-minute practice clock" from memory, ending with the check that proves the outcome.
Step 1 of 2
A forty-minute practice clockSelf-imposed rehearsal
- 10–6 min · Requirements & assumptions. State the latency target (e.g. p95 under 100ms for tab), the scale (millions of DAU) and what good means. Name your assumptions explicitly so you can defend them when pushed.
- 26–16 min · Data flow. Walk the request from keystroke to rendered suggestion: debounce, context gather, prompt build, inference, stream back, accept/reject telemetry. Draw the path in words; mark where the time goes.
- 316–28 min · Latency & cost tradeoffs. This is the heart of the exercise. Smaller model vs quality, cache hit rate vs freshness, speculative vs exact, on-device vs server cost. For each, state what you'd trade and why for this product.
- 428–34 min · Eval. How you'd know it works on real usage, not your demo: acceptance rate, characters-retained-after-N-seconds, A/B on a small traffic slice, guardrails against confidently-wrong suggestions.
- 534–40 min · Failure modes. Cold cache, model timeout, a suggestion that corrupts the buffer, a latency spike under load. Say what degrades gracefully and what must never break.
Latency is the product, not a constraint
For tab prediction, a correct suggestion that lands after the user has typed past it can be less useful than a slightly weaker one that arrives in time. Frame tradeoffs around the user's typing speed so the exercise tests product judgment, not only architecture recall.
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Advanced table
Score against the rubric
Score against the rubric
- Dimension
- Scoping
- What a 5 looks like
- Pinned latency, scale and a definition of good in the first six minutes
- Red flag
- Started drawing boxes before stating the target
- Dimension
- Depth
- What a 5 looks like
- Real mechanisms: caching, speculative decode, retrieval ranking
- Red flag
- Hand-waved “we'd use a model” with no internals
- Dimension
- Tradeoffs
- What a 5 looks like
- Each choice has a stated cost and a reason tied to this product
- Red flag
- Listed options without committing or defending one
- Dimension
- Product awareness
- What a 5 looks like
- Eval, telemetry and failure modes framed around real users
- Red flag
- Designed for a benchmark, never mentioned acceptance or trust
| Dimension | What a 5 looks like | Red flag |
|---|---|---|
| Scoping | Pinned latency, scale and a definition of good in the first six minutes | Started drawing boxes before stating the target |
| Depth | Real mechanisms: caching, speculative decode, retrieval ranking | Hand-waved “we'd use a model” with no internals |
| Tradeoffs | Each choice has a stated cost and a reason tied to this product | Listed options without committing or defending one |
| Product awareness | Eval, telemetry and failure modes framed around real users | Designed for a benchmark, never mentioned acceptance or trust |
Use this rubric to make the practice recording concrete; it is not a published interview scorecard.
Watch out
Designing for peak quality while ignoring latency and inference cost produces a weak product plan. For a high-frequency surface, compare a smaller fast path with selective heavier inference and state how usage data would test the tradeoff.