An AI-generated explanation can sound plausible while quietly changing what the source actually says. A chart without a unit becomes a temperature measurement. Two metrics moving together become a causal story. A process description loses the exception that changes the next action.
One useful review artifact is a source-to-scene map: a small record that connects each important statement to its source, the point where it appears in the video, and the limits on its interpretation.
Disclosure: I build KPainter. An AI agent drafted this article using our public, fictional demonstrations. The map below is a general review template, not a KPainter API format or an automated fact-checking guarantee.
“Explain the spreadsheet” is too broad for a reviewer. “The final completion rate is three percentage points above the example target” is checkable.
For each statement, record:
The video does not need to show this entire record. Keep it beside the source so another reviewer can retrace the statement.
Our fictional workbook has a latest completion rate of 95% and a target of 92%. The difference is 3 percentage points. Calling that “3% growth” changes the calculation and introduces a growth claim that the target comparison does not support.
Here is a compact JSON template for the review record:
{
"source": {
"title": "Operating KPI workbook",
"version": "revision-1",
"location": "KPI Summary!B5:D5",
"kind": "fictional demonstration"
},
"statement": {
"latest": 95,
"target": 92,
"unit": "percent",
"period": "W12",
"difference": 3,
"differenceUnit": "percentage points"
},
"proposedNarration": "The latest completion rate is three percentage points above the example target.",
"limits": [
"Week labels are not calendar dates.",
"The file does not supply raw event records or denominator definitions.",
"This comparison does not explain the cause of a change."
],
"videoSegment": null,
"reviewer": null,
"status": "needs_review"
}
The narration in this JSON is a proposed line, not a quotation from the existing video. Leave the timestamp and reviewer empty until they have actually been assigned. A populated JSON object is not evidence that someone approved the content.
The public workbook case includes the source file, generated video and a source-to-scene map. Its Week Six anomaly is a useful review prompt; the workbook alone cannot establish a root cause.
Our six-slide urban-heat demonstration contains a cooling chart with illustrative values, but the deck does not specify a temperature unit. A reviewer should not silently add °C because it seems likely.
The correct review action is to preserve the illustrative status, identify the missing unit, and ask the source owner before making a measured-temperature claim. The course example maps slides 1–6 to a 2:17 video, so readers can inspect where the explanation draws on each part of the deck.
When a number, threshold or process owner changes, search the map for the affected source locations. Review those statements and their video segments. Then check adjacent scenes: a changed value can also invalidate a comparison, conclusion or next step.
Store the source version and video revision together. Record who reviewed the replacement and which earlier version it supersedes. For operational training, keep the current controlled procedure available to the viewer; a video should not become an unversioned copy of the only authoritative instructions.
Before publishing, ask three questions:
These checks work whether the video is recorded by a person, exported from slides or generated with AI. Which source detail is hardest to keep intact in your own explainer workflow?