AI Workflows can display confidence scores and AI reasoning during workflow execution and moderation.

These features help teams better understand:

Confidence scoring and reasoning improve transparency and help maintain quality control inside automated workflows.

Confidence scores indicate how certain the AI is about the generated result.

Higher confidence usually means:

Lower confidence may indicate:

Confidence scores help reviewers identify which outputs may require closer inspection.

AI reasoning explains why the AI generated specific output.

Reasoning provides visibility into:

This helps moderators better understand how the AI reached a result.

Large workflows may process:

Reviewing every product manually is often unrealistic.

Confidence scores help teams:

AI reasoning improves transparency and trust inside automation pipelines.

Without reasoning, reviewers only see the generated result.

With reasoning, reviewers also understand:

This improves moderation quality and debugging capabilities.

Confidence scores may appear during:

Scores are typically visible inside:

AI reasoning may be visible inside:

Reasoning is often accessible by opening the generated result details.

Example:
Flavor extraction:

This indicates the AI is highly certain the product contains salmon based flavor information.

Example reasoning:
"The product description references salmon based dry cat food intended for adult cats, therefore Flavor was assigned to Salmon and Lifecycle to Adult."

This helps reviewers understand why the extraction was generated.

High confidence outputs often contain:

These outputs may require less manual review.

Low confidence outputs may occur when:

These outputs usually benefit from manual moderation.

Moderators can use confidence scores to prioritize review work.

Example strategy:

This helps scale moderation more efficiently.

A high confidence score does not always mean the output is correct.

Similarly, low confidence does not always mean the output is wrong.

Confidence scores should be used as:

not as absolute truth.

Confidence scores often improve when:

Workflow optimization usually improves both confidence and output quality over time.

Better prompts often produce:

Well structured workflows also improve reasoning reliability.

Low confidence scores may be caused by:

Improving source data quality often improves workflow performance significantly.

Focus moderation effort on:

This improves operational efficiency.

Confidence scoring works best when combined with:

This creates safer automation pipelines.

Different categories often produce different confidence behavior.

Examples:

Focused workflows improve extraction reliability.

Weak prompts often produce:

Prompt optimization is an important part of workflow management.

Example:
A webshop uses Attribute Extraction to detect:

Workflow results:

Moderators focus manual review on Product B because the AI is less certain about the extraction.

Reasoning helps reviewers understand why the AI selected the generated values.

Confidence scores and AI reasoning help businesses:

These features make AI Workflows more understandable, controllable and scalable for large catalog operations.