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The per-token price dropped to one fifth of its previous level while the prompt sets and reported traffic held steady, yet the monthly bill climbed. Build the account of where the money went, then defend the one fix you would ship. This independent, original scenario is designed for Research Engineer, ML / Inference Systems, Software / Infrastructure preparation. Work through the stages in order, explain each decision, and use the review dimensions to test whether the result remains defensible when constraints change.
Longer outputs, retried calls, cache misses, a rerouted model, extra tool invocations, and concurrency that grows when responses slow down each move spend for a different reason. Pick two of them and describe the measurement or experiment that would separate them.
Is every hypothesis paired with a test that could actually reject it?Part of the increase may come from callers that never appear in the traffic dashboard. Explain how you would discover those callers and attribute their share of spend before tuning anything else.
Does the candidate check the denominator before optimizing efficiency?You can ship one change this week: a routing rule, a cache repair, or a cap on tool calls. Choose one, state the recovery you expect, and name the check that would show the cost moved elsewhere instead of disappearing.
Can the candidate commit to an action with an expected effect and a falsifying signal?Use these checks after a first draft. They are review prompts, not six more questions to answer.
Clarifies goals, constraints, and ambiguous requirements.
Protects invariants and handles boundary conditions.
Identifies realistic failures and designs recovery paths.
Makes ownership and transitions explicit.
Provides enough visibility to diagnose and run the system.
Explains what is gained, lost, and intentionally deferred.
Use a different system, project, or situation and check whether the structure still produces a specific, defensible answer.