Low confidence results indicate that the AI is less certain about the generated output.
This does not always mean the output is incorrect, but it may require additional review before synchronization or publication.
Why low confidence happens
Low confidence scores are commonly caused by:
incomplete product data
vague descriptions
missing attributes
unclear terminology
conflicting product information
weak supplier content
Examples
Incomplete descriptions
Example:
"Premium cat food for sensitive cats."
The AI may not have enough information to confidently determine:
flavor
lifecycle
dietary type
Ambiguous product information
Some products may fit multiple categories or interpretations.
This can lower confidence during:
Attribute Extraction
Category Mapping
Translation
Content Enrichment
Missing structured data
Products without attributes or specifications often produce lower confidence results.
How to handle low confidence output
Review the generated result
Inspect:
generated content
extracted values
translations
AI reasoning
Improve source data
Better source content often improves confidence scores significantly.
Examples:
clearer descriptions
structured attributes
consistent terminology
Improve prompts
More specific prompts usually produce:
stronger reasoning
more reliable output
higher confidence scores
Use moderation
Low confidence outputs should usually receive manual review before synchronization.
Important to know
Confidence scores are indicators, not guarantees.
High confidence does not always mean the output is correct
Low confidence does not always mean the output is wrong
Confidence scoring should be used to prioritize moderation effort.
Best practices
Focus manual review on low confidence results
Use category specific workflows
Improve prompts continuously
Keep supplier data clean and structured
Combine confidence scoring with moderation