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High-volume document review needs extraction, model judgment, evidence links, and a human queue whose decisions can be defended later. Design the pipeline and the versioning that keep it auditable. This independent, original scenario is designed for ML / Inference Systems, Agents / Evals, 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.
Describe how documents are ingested, how text and structure are extracted, and how each model-produced claim is bound to the part of the document that supports it. Say what happens when no supporting region is found.
Is an unsupported claim handled explicitly rather than smoothed over?Define routing and priority for the human queue, how disagreement between model and reviewer is resolved, and which corrections return as signal to the model.
Does the human step produce learning instead of only throughput?A model or policy changes while earlier documents are still in flight. Explain versioning, re-review, and what an auditor can reconstruct a year later.
Can the candidate reconstruct a decision from stored history alone?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.