Attribute Extraction prompts help AI identify and fill structured product data automatically.
Well written extraction prompts improve:
attribute accuracy
catalog consistency
filter quality
marketplace readiness
moderation efficiency
Poor prompts often create:
incorrect attributes
inconsistent values
hallucinated data
unreliable extraction
This article explains how to create stronger extraction prompts for AI Workflows.
What extraction prompts do
Extraction prompts instruct the AI to:
identify specific attributes
extract values from product content
normalize results
avoid unsupported assumptions
Extraction workflows are commonly used for:
supplier catalog enrichment
missing attribute generation
layered navigation improvements
marketplace preparation
structured ecommerce data
Recommended extraction prompt structure
Strong extraction prompts usually contain:
Role
Input data
Attributes to extract
Rules
Output requirements
Example extraction structure
Role
You are a highly precise product attribute extraction engine.
Input data
Product Name: {{name}}
EAN: {{ean}}
Description: {{description}}
Attributes to extract
Flavor
Lifecycle
Rules
Only extract explicitly mentioned values
Do not guess missing information
Normalize values into consistent terminology
Return empty values when information is missing
Output
Return extracted values only.
This structure creates much more reliable extraction behavior.
Use dynamic variables
Extraction templates should use dynamic product variables whenever possible.
Examples:
{{name}}
{{sku}}
{{ean}}
{{description}}
This allows the workflow to automatically adapt prompts for every product.
Be specific about attributes
Weak prompt:
Extract product information.
Better prompt:
Extract the following attributes:
Flavor
Lifecycle
Material
Dimensions
Specific prompts improve extraction reliability significantly.
Add normalization rules
Normalization rules improve consistency across large catalogs.
Example:
“rundvlees” → “Rund”
“volwassen” → “Adult”
“krokante brokken” → “Droogvoer”
Normalization helps improve:
filters
layered navigation
SEO consistency
marketplace structure
Prevent hallucinations
Extraction prompts should clearly define strict behavior.
Examples:
Do not guess missing values
Only use explicitly mentioned information
Return empty values if data is unavailable
Do not invent attributes
Strict prompts reduce incorrect AI output.
Use category specific prompts
Different product categories require different extraction logic.
Examples:
Fashion
Material
Fit
Sleeve length
Electronics
Voltage
Connectivity
Compatibility
Pet food
Flavor
Lifecycle
Dietary type
Category focused prompts improve extraction quality significantly.
Use external context carefully
Some extraction workflows may use:
competitor websites
supplier sources
public product pages
Example:
Search official product pages before using fallback descriptions.
This can improve extraction quality when supplier data is incomplete.
Example advanced extraction prompt
Example:
You are a highly precise Data Extraction Engine specialized in Dutch Pet Care products.
Product Name: {{name}}
SKU: {{sku}}
EAN: {{ean}}
Description: {{description}}
Extract:
smaak
levensfase
Rules:
Only extract explicitly mentioned values
Do not guess missing information
Normalize values consistently
Return empty values if information is unavailable
Normalization:
“rundvlees” → “Rund”
“volwassen” → “Adult”
Language:
Return all values in Dutch.
This creates a much more controlled extraction workflow.
Test extraction prompts carefully
Before running extraction workflows on large catalogs:
test prompts on sample products
inspect extracted values
review confidence scores
validate normalization behavior
inspect AI reasoning
Testing is strongly recommended before large scale execution.
Common extraction prompt mistakes
Overly broad prompts
Weak prompts often create:
inconsistent values
unsupported assumptions
irrelevant output
Missing normalization logic
Without normalization, catalogs may contain inconsistent values.
Example:
Rundvlees
Rund
Beef
Rund vlees
Normalization prevents this.
Too many attributes in one workflow
Large extraction prompts can reduce reliability.
Instead:
split workflows by category
focus on related attributes
create specialized extraction logic
Best practices
Use clear attribute lists
Add normalization rules
Avoid guessing behavior
Test prompts before scaling
Use category specific workflows
Improve prompts continuously
Example workflow
Example:
Trigger selects pet food products missing attributes.
Attribute Extraction extracts:
Flavor
Lifecycle
Content Enrichment generates:
Shopping descriptions
SEO titles
Translation localizes content into German.
Moderation reviews output before synchronization.
This creates a structured enrichment pipeline using extraction prompts.