Prompts control how AI behaves inside AI Workflows.
They define:
what the AI should do
which data should be used
how output should be structured
which rules the AI must follow
Prompts are one of the most important parts of workflow configuration because they directly influence:
output quality
extraction accuracy
SEO performance
translation consistency
moderation workload
Well written prompts create more reliable and scalable automation pipelines.
Where prompts are used
Prompts can be used in multiple workflow actions.
Examples:
Attribute Extraction
Content Enrichment
Translation
Category Mapping
Each action uses prompts differently depending on the workflow goal.
Prompts vs templates
A prompt is the instruction given to the AI.
Example:
Generate an SEO optimized shopping description using a professional ecommerce tone focused on pet nutrition.
A template is a reusable prompt containing dynamic variables.
Example:
Product Name: {{name}}
Description: {{description}}
Templates allow workflows to automatically generate contextual prompts for every product.
Using variables inside prompts
Templates can include dynamic product variables.
Examples:
{{name}}
{{sku}}
{{ean}}
{{description}}
These variables are automatically replaced with product data during workflow execution.
Example template:
Product Name: {{name}}
EAN: {{ean}}
Description: {{description}}
This allows prompts to scale across large catalogs automatically.
Recommended prompt structure
Strong prompts usually contain:
Goal
Input data
Rules
Output requirements
Fallback behavior
Example structure:
Goal
Extract product attributes from the product description.
Input
Product Name: {{name}}
Description: {{description}}
Rules
Only extract explicitly mentioned values
Do not guess missing information
Normalize attribute values
Return empty values when data is missing
Output
Return:
Flavor
Lifecycle
This structure creates more predictable AI behavior.
Example Attribute Extraction prompt
Example:
You are a highly precise product attribute extraction engine.
Product Name: {{name}}
EAN: {{ean}}
Description: {{description}}
Extract the following attributes:
Flavor
Lifecycle
Rules:
Only extract explicitly mentioned information
Do not guess missing values
Normalize values into consistent terminology
Return empty values if information is missing
This prompt focuses the AI on structured extraction instead of content generation.
Example Content Enrichment prompt
Example:
Generate an SEO optimized shopping description using a professional ecommerce tone focused on pet nutrition.
Requirements:
Maximum 500 characters
Use natural ecommerce language
Highlight nutritional benefits
Avoid keyword stuffing
Write in Dutch
This creates more controlled enrichment output.
Example Translation prompt
Example:
Translate the Shopping Description to German using natural ecommerce language suitable for online product pages.
Requirements:
Preserve formatting
Keep product names untranslated
Use professional ecommerce terminology
Avoid literal translations
This improves localization quality significantly.
Using normalization rules
Prompts can contain normalization logic to improve consistency.
Example:
“rundvlees” → “Rund”
“volwassen” → “Adult”
“krokante brokken” → “Droogvoer”
Normalization helps improve:
catalog consistency
filtering
layered navigation
marketplace compatibility
Prompt strictness
Good prompts clearly define limitations.
Examples:
Do not guess missing values
Only use verified information
Return empty values if data is unavailable
Avoid generating unsupported claims
Strict prompts reduce hallucinations and incorrect output.
Using prompts in workflow chains
Prompts become more powerful when workflows combine multiple actions.
Example workflow:
Attribute Extraction → Content Enrichment → Translation
In this workflow:
extracted attributes improve enrichment quality
enriched content improves translation quality
This creates more contextual and reliable output across the workflow pipeline.
Prompt testing
Before running workflows on large catalogs:
test prompts on sample products
review generated output
inspect confidence scores
validate formatting
review AI reasoning
Testing helps prevent large scale workflow issues.
Prompt best practices
Be specific
Weak prompt:
Generate a product description.
Better prompt:
Generate a Google Shopping optimized product description using a professional ecommerce tone focused on pet nutrition.
Specific prompts create more reliable output.
Use category specific prompts
Different categories often require different prompt strategies.
Examples:
Fashion
Electronics
Pet food
Supplements
Category specific prompts improve output quality significantly.
Avoid overly broad instructions
Large generic prompts often create:
vague descriptions
inconsistent formatting
unstable output
Focused prompts usually perform better.
Improve prompts continuously
Prompt optimization is an ongoing process.
You may improve prompts to:
reduce moderation workload
improve SEO quality
improve extraction accuracy
increase translation consistency
create more structured output
Example workflow using prompts
Example:
Trigger selects products missing descriptions.
Attribute Extraction prompt extracts:
Flavor
Lifecycle
Content Enrichment prompt generates:
Shopping descriptions
SEO titles
Translation prompt localizes content into German.
Moderation reviews output before synchronization.
This creates a scalable AI driven enrichment pipeline.