An AI data enrichment review workflow should let a person decide whether a proposed change is correct without repeating the agent's whole investigation. Show the original value, proposed value, relevant evidence, and the consequence of approval together.
The reviewer should also be able to correct the system's understanding, not just accept or reject individual cells forever.
Approve the meaning of the data first
Before a worker starts, present the schema interpretation in ordinary language. For example: “A product describes a model. A listing is one seller's offer. Inspection results belong to the physical item. Price and stock come from the offer table.”
Let the team correct this understanding on the page. Save the approved version and associate future jobs with it. If an important table relationship changes, pause affected work and request a new approval.
This step prevents a common class of mistake: enriching the right fact onto the wrong entity.
Make one proposal easy to judge
An illustrative proposal adds an 18 L capacity to a tote listing. The review card should include the original empty field, proposed capacity with its unit, the exact matched model, and the manufacturer evidence.
Show whether the source record changed since the proposal was prepared. A reviewer should not approve research for yesterday's model number after a seller has replaced it today.
| Review element | Question it answers |
|---|---|
| Original and proposed values | What will change? |
| Entity and variant | Is this the correct item? |
| Evidence and retrieval time | Why should I believe the value? |
| Source version | Does this proposal still apply? |
| Publication scope | Where will approval make it visible? |
| Decision history | Who approved or corrected it? |
Distinguish rejection from correction
A reviewer may reject the value, reject the source, or correct a general rule. Those are different feedback types. “This is package width” should correct the interpretation and invalidate affected proposals, not merely hide one row.
Store durable feedback in a versioned policy or schema understanding. Do not rely on a chat transcript as the only place an important rule exists. The next job should be able to identify which approved instruction it used.
A correction should not automatically authorize a broader scope of work. Changing how dimensions are interpreted does not grant permission to edit price or condition.
Support batches without hiding differences
Group proposals that share a rule, source, and risk profile. A reviewer can inspect examples before approving a narrow batch, such as a known brand-spelling correction.
Keep high-consequence claims separate. Product identity, condition, compatibility, and authenticity should not ride along with a batch of formatting fixes. Make batch scope and excluded records visible before the decision.
Require a reason for overrides where it will help future work, but avoid turning every routine approval into a form-filling exercise.
Sample approved work as well as exceptions
Review queues show what the system is unsure about. They do not reveal all confident errors. Independently sample approved and automatically published changes to estimate missed problems.
Track rejection reasons, time per review, reviewer disagreement, stale proposals, and corrections found after publication. Use those results to adjust rules and thresholds. A declining review queue is useful only if quality remains acceptable.
Make withdrawal ordinary
A reviewer should be able to withdraw an enhancement or batch and see the source value restored in the resolved read. Preserve the decision history rather than deleting the record of what happened.
Goodfolk's proposed workflow starts with approved schema understanding and uses that context for the four workers. In a pilot, test the correction and withdrawal paths as carefully as the approval path.
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