Why AI Must Not Guess Technical Product Data
An AI hallucination occurs when a model produces information that sounds plausible but is not supported by the available evidence.
In product data, that can mean inventing a specification, compatibility claim, identifier or product feature that does not belong in the record.
The problem is not unusual wording. The problem is unsupported factual content being treated as catalog data.
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{{ c.text }}Which fields carry the highest hallucination risk
Examples of high-risk hallucinations include an unverified diameter, material, pressure rating, OEM number or compatible machine model.
Use real platform examples here only when they are documented.
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{{ c.text }}A value tied to evidence, not to likelihood
Source grounding means that a technical value is tied to relevant evidence rather than generated because it appears likely. Provenance makes that relationship reviewable.
If a value cannot be grounded with the required level of evidence, leaving the field incomplete can be safer than filling it with a plausible guess.
Several checks before product data is accepted
Validation can check schema fit, identifier consistency, source agreement, units and other rules before product data is accepted.
No single validation layer removes all risk, which is why the architecture combines multiple checks and manual review where necessary.