An AI-generated technical illustration should never pass review because it “looks right.” It should pass because a reviewer has compared it with the product, the procedure, and the controlled source information.
The following checklist works for assembly guides, installation diagrams, maintenance procedures, troubleshooting figures, packaging inserts, and early IFU drafts. It is deliberately short enough to use on every image.
1. Product identity
Is this unmistakably the correct product revision? Check the silhouette, controls, ports, vents, feet, hinges, handles, and distinguishing features. A polished image of the wrong revision is still wrong.
2. Stable geometry
Does the product keep the same proportions and part locations across panels? Watch for holes that move, handles that change shape, duplicated buttons, and seams that appear only in one step.
3. Correct starting state
Does the panel begin where the previous panel ended? Missing or prematurely installed parts break the sequence even when each individual picture looks plausible.
4. One clear action
Can the main action be stated with one verb? If the reader must insert, rotate, and tighten in the same panel, split the step unless the actions truly occur together.
5. Arrow logic
Trace every arrow. Does it start at the moving part, follow the real motion, and end at the correct destination? Use curved arrows for rotation and avoid decorative arrows with no physical meaning.
6. Fastener and part count
Count screws, washers, clips, and repeated parts. Confirm their shape and quantity against the BOM or approved procedure. An AI model cannot be the source of record for hardware counts.
7. Orientation and handedness
Check front/back, left/right, finished/unfinished faces, connector keying, and mirrored parts. Add a correct/incorrect comparison when a part can be installed in a believable but wrong orientation.
8. Visibility and hand placement
Do hands, tools, arrows, or callouts hide the exact feature being explained? The work area must stay visible. Hand placement should also be physically possible and avoid implying an unsafe grip.
9. Warnings and controlled facts
Verify warning text, PPE, torque, dimensions, voltage, temperature, and disposal instructions against approved sources. Never infer certification marks or regulatory claims from appearance.
10. Text and symbol accuracy
Read every character. Remove invented labels, fake logos, decorative pseudo-text, and ambiguous icons. If a symbol has a controlled meaning, confirm that the correct symbol is being used.
11. Cross-figure consistency
Compare the full set, not only one panel. Stroke hierarchy, viewpoint family, arrow style, accent colors, callout shapes, and detail-circle treatment should feel like one visual system.
12. Delivery quality
Preview the figure at its final size. Are thin lines still visible? Are raster edges sharp enough? If the downstream team must edit line weights or callouts, verify that the SVG contains real strokes rather than one embedded bitmap.
Make review evidence visible
Record what changed after review and which source confirmed it. For safety- or compliance-sensitive material, identify the person or function that approved the final figure. AI can accelerate draft production, but traceable human review is what turns a draft into controlled documentation.
ManualFig AI keeps image versions together and supports targeted edits, PNG export, and SVG conversion, which makes it easier to correct a failed checklist item without rebuilding the full figure.
Run your own product figure through the ManualFig AI workflow.
Print the checklist, add it to your review template, or turn it into twelve acceptance fields in your documentation system. The format matters less than using the same standard every time.
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