AI failure guide

How to stop AI hallucinations in product catalogs.

Catalog mistakes happen when item names, units, and descriptions are vague. The model fills gaps confidently unless the workflow adds cleanup, validation, and review.

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Why it happens
Descriptions are incomplete or inconsistent.
Units and packaging terms are vague.
The workflow has no confidence scoring or review gate.
What it breaks
Wrong product names appear in the catalog.
Units get normalized incorrectly.
Teams publish bad data and create ordering mistakes.
What a better workflow does
Normalize fields before generation.
Flag low-confidence rows instead of forcing a guess.
Return a clean export plus a short review list.
Best next step

If the output is a catalog that people will order from, the workflow needs cleanup and validation before publish.

Practical path: Start with the ugly supplier sheet, clean the fields, score the risky rows, and deliver a publish-ready export.
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