Human Review Notes from 1,000 Generated Product Descriptions

Describe a review protocol using a clearly labelled illustrative batch. The useful boundary is a repeatable workflow with named inputs, explicit checks, and a release decision that another contributor can inspect. The examples below are illustrative procedure, not a claim about a particular customer or production result.

Set the operating boundary

State that the 1,000-example batch is an editorial scenario used to explain the method. Treat that requirement as a recorded engineering decision rather than an informal cleanup step. Start with a small, versioned input set and keep the original source identifier beside every transformed record. That lets a reviewer trace an unexpected output back to the data, configuration, or evaluation rule that produced it.

The implementation should separate deterministic checks from judgment calls. Deterministic checks can reject missing fields, malformed files, invalid ranges, and inconsistent identifiers. Judgment calls need a documented review rule, a sample of borderline cases, and a reason for keeping or excluding them. Mixing the two creates silent changes that are hard to reproduce later.

Factuality, attribute coverage, prohibited claims, tone, and duplication criteria

Define factuality, attribute coverage, prohibited claims, tone, and duplication criteria. Treat that requirement as a recorded engineering decision rather than an informal cleanup step. Start with a small, versioned input set and keep the original source identifier beside every transformed record. That lets a reviewer trace an unexpected output back to the data, configuration, or evaluation rule that produced it.

Record the result in a small manifest: source version, transformation version, input digest, output digest, reviewer, and timestamp. A compact manifest is more useful than a long narrative when a later run needs to be compared with this one.

Create a compact reviewer form and severity scale

Create a compact reviewer form and severity scale. Treat that requirement as a recorded engineering decision rather than an informal cleanup step. Start with a small, versioned input set and keep the original source identifier beside every transformed record. That lets a reviewer trace an unexpected output back to the data, configuration, or evaluation rule that produced it.

Calibrate two reviewers on a shared subset

Calibrate two reviewers on a shared subset. Treat that requirement as a recorded engineering decision rather than an informal cleanup step. Start with a small, versioned input set and keep the original source identifier beside every transformed record. That lets a reviewer trace an unexpected output back to the data, configuration, or evaluation rule that produced it.

Summarise recurring error categories without fabricated business impact

Summarise recurring error categories without fabricated business impact. Treat that requirement as a recorded engineering decision rather than an informal cleanup step. Start with a small, versioned input set and keep the original source identifier beside every transformed record. That lets a reviewer trace an unexpected output back to the data, configuration, or evaluation rule that produced it.

Turn review findings into dataset and prompt changes

Turn review findings into dataset and prompt changes. Treat that requirement as a recorded engineering decision rather than an informal cleanup step. Start with a small, versioned input set and keep the original source identifier beside every transformed record. That lets a reviewer trace an unexpected output back to the data, configuration, or evaluation rule that produced it.

Practical check

Markdown review rubric and CSV column specification. The following minimal check is intentionally small enough to run before a longer workflow. Expand it only after its output identifies the missing condition or failure category.

record_id,criterion,severity,reviewer_note
example-001,missing-required-field,blocker,Reject before release

Review and release criteria

A result is ready for the next stage only when the required fields, split boundaries, and evaluation evidence agree. Keep a rejected sample set as well as accepted output; otherwise the same edge case will return as an unexplained regression. Presenting illustrative counts as audited company data. The correct response to uncertainty is to label the limitation and add a focused validation case, not to hide it behind a blended score.

Before publishing, verify the data digest, configuration, expected output shape, and the most important rare-case slice. The final registry note should state what changed, what was checked, and what remains outside the adapter's intended scope. That makes the next iteration a comparison instead of a guess.