Edit defects, not product facts.
That sounds obvious, but it is where a lot of AI product-photo workflows start to drift.
Dust, weak lighting, messy background, uneven crop, compression artifacts, and minor shadow problems are usually safe editing targets. They make the image harder to read, but they are not part of the product.
Color, material, label text, edge shape, transparency, included parts, connector shape, fabric texture, gemstone tone, and product scale are different. Those details help the buyer understand what they are buying.
For LoomaDesign content this week, I used that rule to separate product retouching into SKU risk levels:
Low risk:
- matte plastic products
- simple desk accessories
- basic storage items
- products with clean shapes and limited label detail
Medium risk:
- apparel
- bags
- beauty packaging
- home goods
- small electronics accessories
High risk:
- jewelry
- watches
- glass
- reflective products
- food and beverage
- luxury packaging
- hero products used in ads
AI retouching is useful when the task is cleanup and consistency. It gets risky when the edit changes product truth.
I wrote the fuller guide here:
https://loomadesign.ai/en/blog/product-retouching-for-ecommerce-sku-risk
Related LoomaDesign reads:
https://loomadesign.ai/en/blog/ai-product-photo-retouching-tools-ecommerce
https://loomadesign.ai/en/blog/color-correction-for-ecommerce-product-images
https://loomadesign.ai/en/blog/how-to-fix-pixelated-product-photos
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