How to Validate AI-Generated Product Data
Validation should test an AI-generated value against the product schema, plausible ranges, source evidence, identifiers, units, record consistency and review rules before the value is accepted.
The goal is not to make a generated value look plausible. The goal is to determine whether it is supported well enough to enter the product record.
See validation methodologyTechnical attribute candidate
fieldcategory-specific attributeCHECKvaluegenerated candidateHOLDsourceevidence requiredVERIFYdecisionnot accepted yetPENDINGFirst check whether the proposed value belongs in the field at all.
Schema validation confirms the expected field, value type, allowed structure and category-specific requirements before factual evaluation begins.
Candidate checked against schema constraints
fieldExpected technical attributeexists in family schematypeNumeric + unitcorrect structural typecandidateFree text in numeric fieldstructural failuredecisionReject before factual reviewschema gate stops the valueUse ranges to catch impossible or suspicious values — not to prove truth.
A range check can identify values outside plausible boundaries for a product family. A value inside the range still needs evidence.
Trace the claim back to evidence that actually supports it.
A value is not validated because a model can repeat it. The evidence view should show which product, which field, which source and what decision follows from that evidence.
Kverneland AC820825 Fan Impeller
productAC820825weight1.74 kgproductAC820825weight2.60 kgreasonsame OEM reference · conflicting valuesoutputfield excluded until resolvedWhen sources disagree, preserve the conflict instead of hiding it.
Cross-source validation compares evidence for the same field and records agreement, disagreement or insufficient support before acceptance.
Decision ledger
agreementnot establishedconflictrecorded explicitlyactionroute to reviewVerify that identifiers resolve to the same product context.
SKU, MPN, GTIN and OEM-related references should not be treated as interchangeable. Validation checks their format and relationship to the resolved product.
Incoming product record
SKUinternal catalog IDMPNmanufacturer referenceGTINtrade identifier where applicableOEMrelationship / reference contextIdentifiers agree with entity
entityone product / variantstatusidentifier context validatedNormalize units before comparing or accepting technical values.
A value can be numerically correct and still be unusable if the unit is missing, ambiguous or inconsistent with the schema.
source A42 mmsource B4.2 cmsource C0.042 mValidate the record as a system, not just field by field.
A proposed value can pass an individual check and still contradict identifiers, category context, dimensions or other attributes in the same record.
Confidence routes work; evidence decides whether a fact is acceptable.
ENRIVAQ uses task-specific confidence signals internally, but the public workflow does not present one score as the probability that a technical fact is true. Product identity, provenance, canonical mapping and conflicts matter together.
Ambiguous or high-risk cases need a review state with evidence attached.
Human review is most useful when the reviewer sees the proposed value, source evidence, conflicts and validation results in one place.
Proposed product value
candidateAI-generated field valuesource Asupports candidatesource Bconflicts with candidaterangeplausibleconfidenceinsufficient for automatic acceptanceRelease only after the required validation gates have a recorded outcome.
A practical checklist should show each layer separately so reviewers can see why a value was accepted, rejected or held.
PASS / FAILPASS / FLAGVERIFYSTATEPASS / FAILPASS / FIXPASS / REVIEWROUTEDECISIONNo silent acceptance.
The validation record should make clear what was checked and why the field entered the catalog.
Common questions about validating AI-generated product data.
Confidence is a routing signal, not proof.
A high score should still be interpreted with evidence, consistency checks and the acceptance policy for the field.
Run the framework on a real product record.
The useful test is not whether the framework looks complete on paper, but whether it can explain the decision on an actual product field.