Human-in-the-loop review patterns

Not every low-confidence field deserves the same urgency or reviewer. Queue design should consider business consequence, document type, and whether a human can resolve the ambiguity quickly with the context available.

Use the section on Confidence-based routing as the decision frame. The learner should explain when it matters, who owns the decision, what state they would inspect first, and how that state supports the lesson objective: identify when human intervention should happen in an automation flow.

Evidence should come from document intake, provider hint, extraction confidence, review queue state, import audit, alias handling, or downstream workflow result. For Confidence-based routing, a strong answer names the visible cue, record, status, or reference that supports the next step and states what would pause the learner.

Reviewers make decisions with more context when they can see the extracted values, original source, policy context, and the consequence of approval in one place. Human-in-the-loop does not mean manual chaos; it means targeted, well-supported decision making.

Turn the section on Review as decision support into a realistic example. Ask the learner to describe the situation they are responding to, the first surface they would open, the cue they expect to find, and what they would do if that cue is missing.

For Review as decision support, the learner should point to the specific page, record, status, or note that separates evidence from assumption before moving to the next step.

The Aliases tab is the governed master-data bridge for OCR review. Supplier, customer, and product aliases let the team map ambiguous extracted text to a known target entity instead of re-solving the same naming problem on every document.

Use aliases deliberately. Alias text, target entity, confidence boost, and active state all change how future OCR matches are interpreted, so this tab belongs to controlled exception improvement rather than casual cleanup.

Use this section to confirm the learner understands more than the page label. They should connect Alias governance as a review accelerator to the business state, owner, and consequence behind it.

Run the lesson as an automation quality review. Start with the practical task: identify when human intervention should happen in an automation flow. Ask the learner to name the role, surface, evidence, and state they would inspect before taking action.

Evidence should come from document intake, provider hint, extraction confidence, review queue state, import audit, alias handling, or downstream workflow result. The practice should end with the learner connecting the action back to the lesson summary: show how review queues protect automation quality by routing ambiguous cases to the right people with clear context.

Close the exercise by asking the learner to restate the objective in operational terms: identify when human intervention should happen in an automation flow. They should name what changed, what remains uncertain, and which surface or owner takes the next step.

Do not let automation quality be judged by volume alone. Confidence, review routing, import evidence, and exception resolution matter more than raw document count. In this lesson, watch for that risk while learners work on this objective: identify when human intervention should happen in an automation flow.

Do not mark the lesson complete because the learner can repeat terms. Completion means they can explain why manual review should be targeted rather than universal and describe why the lesson matters in real work.

Review the answer for skipped ownership, missing evidence, or vague next steps. If the learner cannot explain why manual review should be targeted rather than universal, keep the lesson in practice mode before marking it complete.