Design a scorecard for when to optimize prompts, models, or infrastructure first.
Instruction: Explain how you would decide where to focus optimization work in an AI system. Fictional mean latency trace totals 2.0 s: queue 1.2 s, retrieval 0.2 s, prefill 0.2 s, decode 0.4 s. Task success is 95%. A prompt reduction can at best halve prefill; a scheduling experiment may halve queue time. Compute their ideal separate gains and choose the first controlled experiment.
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I would use a scorecard to prioritize investigation, then verify the bottleneck with traces and a controlled comparison. A symptom suggests a layer, but does not prove its cause...
Your preparation path
Work through these questions in order. Read the answer aloud, then explain it in your own words.
1. Start with the foundations
Build the vocabulary and explain the core decisions.
2. Apply it to a real workflow
Practice diagnosis, validation, and everyday tradeoffs.
- What signals matter most when optimizing cost without hiding quality loss? Free sample
- Traffic spikes create queueing even though the model service itself is healthy. How would you investigate? Member answer
- The product is fast on short prompts and unstable on long tool-using requests. What would you inspect? Member answer
3. Prepare for senior discussions
Explain failure boundaries, recovery, and production choices.
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