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Choosing the right field type is crucial. The field type determines what kind of data you can store, whether AI can help generate it, and how you’ll use it downstream (filtering, reporting, syncing to channels). This guide covers every field type with examples, configuration details, and decision-making guidance.

Single Line Text (T icon)

What it is: The most flexible field type. Stores text up to a certain character limit. Can be AI-generated or manually entered. Best for:
  • Product descriptions and titles
  • Specifications and technical details
  • Instructions (care, assembly, usage)
  • Material compositions
  • Any text-based data that varies by product
AI Support: Yes. You can write a prompt to have the AI generate or extract text. Configuration:
  • Prompt field: Write instructions for the AI (e.g., “Generate a 2-3 sentence product description for {{product_title}}”)
  • Acceptable Values: Optional. Use if you want to constrain output to a specific list
  • Product Data Context: The AI can reference other attributes via {{ }} tags
  • Image Source: The AI can see your product images to inform text generation
Example use cases:
  • Product Description — “Write a compelling 2-3 sentence description of {{product_title}} highlighting {{material}} and {{intended_use}}.”
  • Care Instructions — “Extract or write care instructions for {{product_title}}. Format as a bulleted list.”
  • Key Features — “List 3-4 key features of {{product_title}}. Be specific and avoid marketing hype.”
Validation:
  • Single Line Text typically has a character limit (e.g., 500 characters); Merchkit will warn you if you exceed it
  • No calculation or formula needed
Common mistakes:
  • Writing vague prompts (see 3.4 for prompt best practices)
  • Using when a different field type would be better (e.g., use Number for numeric data, not Text)

Number (# icon)

What it is: Stores numeric values only. Can be AI-generated (extracted from source documents) or manually entered. Best for:
  • Prices and costs (MSRP, wholesale price, cost)
  • Dimensions and measurements (weight, height, length)
  • Quantities and counts
  • Time-based values (lead time in days, warranty period in months)
  • Any data that’s fundamentally numeric
AI Support: Yes. The AI can extract numeric values from source documents or generate them based on context. Configuration:
  • Prompt field: Write instructions (e.g., “Extract the MSRP from the source document. Return only the numeric value.”)
  • Acceptable Values: Optional, but unusual for numeric fields (used only if you want to restrict to specific numbers)
  • Product Data Context: AI can reference other attributes
  • Image Source: AI can analyze images to extract dimensions or measurements
Example use cases:
  • MSRP — “Extract the MSRP (list price) from the source document for {{product_title}}. Return only the numeric value (e.g., 29.99).”
  • Weight (kg) — “Extract the product weight in kilograms from {{product_title}} source documents. Return only the number.”
  • Lead Time (Days) — “Based on {{vendor_name}} inventory and shipping policy, estimate the lead time in days. Return an integer.”
Validation:
  • Returns only numeric values
  • You can set min/max constraints (e.g., MSRP must be > 0)
  • Non-numeric input is rejected
Common mistakes:
  • Using Text field type for data that should be numeric (harder to sort, filter, and calculate later)
  • Asking the AI to return formatted values like “$29.99” or “3 days” instead of just the number
  • Forgetting to specify units in the prompt

Image (camera icon)

What it is: AI-generated images using the Nano Banana image generation model. The AI uses your product images as a reference and your prompt to create new visuals. Best for:
  • Dimension line drawings
  • Lifestyle renders
  • Compliance images (e.g., product showing size comparisons)
  • Alternate product angles
  • Visual specifications
AI Support: Yes, fully AI-generated. The model analyzes your product image(s) and creates new visuals based on your prompt. How it works:
  1. The Image field reads your product image(s) as input
  2. You write a prompt describing what visual to generate (e.g., “Create a dimension line drawing with measurements”)
  3. The AI (Nano Banana model) generates a new image
  4. The generated image replaces or supplements your existing product photo
Configuration:
  • Prompt field: Write detailed visual instructions (e.g., “Create a dimension line drawing showing the product’s height, width, and depth in centimeters. Use a clean white background and include measurement labels.”)
  • Source image selection: Choose which product image(s) the AI should use as reference
  • Acceptable Values: Not applicable for images
Example use cases:
  • Dimension Diagram — “Generate a dimension line drawing of {{product_title}} showing height, width, and depth. Include measurement labels in cm. Use white background.”
  • Lifestyle Render — “Generate a lifestyle image showing {{product_title}} in use. Show {{intended_use}} context. Maintain {{brand}} colors and style.”
  • Size Comparison — “Generate an image showing {{product_title}} next to a standard object (e.g., ruler, coin) for scale reference.”
Validation:
  • Image generation may take longer than text generation
  • The AI will attempt to match your source image style
  • Output quality depends on prompt clarity and input image quality
Limitations:
  • Image generation is more resource-intensive than text
  • Style consistency can vary across multiple generated images
  • Not all prompts will produce high-quality results; test and iterate
Common mistakes:
  • Vague visual descriptions (be specific: “3D isometric view with measurements” not “make it look good”)
  • Unrealistic expectations (the model does its best but can’t guarantee photorealism)
  • Not testing on a few products before running across your entire catalog

Web Preview

Build reusable HTML templates populated from your catalog fields, then inspect the rendered text and images in a preview dialog. Use your own layouts and attribute references to adapt a template to the content you need. For example, you can build:
  • Spec sheets and tear sheets with product images, dimensions, materials, and technical details.
  • Waymore and Amazon A+ content blocks to review visually before publishing each piece through your channel workflow.
  • A dealer sell sheet with a product photo, key features, and SKU.
  • A care or installation one-pager using reviewed instructions already in your catalog.
  • A material and finish overview for a product with those details in its catalog fields.
These are ideas for templates you can author and expand, rather than a built-in template library. Hover over a supported HTML cell and select the eye icon to open the preview. Document-style HTML content supports previews; a plain-text description does not automatically become a preview layout. To save a sheet for an offline catalog, select Print / Save as PDF, then choose Save as PDF in your browser’s print dialog. Review the output before sharing it. Rich-content previews help you check the layout visually; publish the pieces through your usual channel workflow.

Formula (ruler icon)

What it is: A deterministic calculation field. A formula calculates a value from other attributes using a repeatable rule, without AI judgment. Best for:
  • Calculated fields that follow a fixed rule
  • Tax, margin and discount calculations
  • Unit conversions
  • Text extraction and deterministic validation
AI Support: No. The same expression and inputs produce the same result. How it works:
  1. Reference attributes by their keys in double braces, such as {{msrp}}.
  2. When the formula is evaluated, the expression calculates a result from those input values.
  3. Review the stored result against the inputs before using it in a channel export.
Configuration:
  1. Open the attribute’s Configure Attribute panel.
  2. Turn Use AI off and select Formula under Field Type.
  3. Enter the expression in Formula Expression, using attribute keys in double braces.
  4. Review validation and click Save changes.
For example, ROUND({{cost}} * 1.4, 2) calculates a price from cost. REGEXEXTRACT({{supplier_notes}}, "Box 1: ([^;]+)") extracts one box’s measurements from structured supplier notes. Use your workspace’s actual attribute keys. A valid expression does not guarantee that the input data is complete or accurate. Check the resulting value on a sample product before exporting it; if a stored result is missing or stale, do not assume it is ready to publish. Example use cases: Replace these keys with the actual attribute keys in your workspace. The example results assume msrp = 100, cost = 60, weight = 12, brand = "Acme" and product_id = "CHAIR-01". Markup on cost and margin as a share of selling price are different calculations. Choose the rule your team intends and confirm the units and input values before using a result. Using Formulas for Validation Use a formula for checks with a precise rule. For example, IF({{weight}} > 0, "Valid", "INVALID: weight must be positive") flags a zero or negative weight. With weight = 12, it returns Valid; with weight = 0, it returns INVALID: weight must be positive. Other deterministic checks can compare several fields:
  • Volume Check: IF({{length}} * {{width}} * {{height}} = {{volume}}, "OK", "MISMATCH"). With dimensions 2, 3, 4 and volume 24, the result is OK. Use consistent units and an appropriate tolerance for rounded measurements.
  • Required Field Coverage: IF(AND(NOT(ISBLANK({{material}})), NOT(ISBLANK({{weight}})), NOT(ISBLANK({{dimensions}}))), "Complete", "Missing required fields"). This checks presence for the named fields; it does not establish that their values meet destination rules.
  • SKU Format Check: use REGEXEXTRACT with your actual SKU pattern, then inspect blank results. See the function reference for no-match behavior.
  • Quality Score: if you maintain explicit validation or review attributes, a formula can combine those values into a score. Do not assume that AI confidence or source freshness metadata is automatically available as formula inputs.
A formula checks the rule you provide; it cannot establish that a supplier’s measurement is true. For a judgment such as whether a material is plausible for a category, give the AI prompt the relevant evidence and review its answer. Use formulas for deterministic calculations, unit conversions and comparisons; use AI where the task requires interpretation. Validation: Review the formula editor’s validation feedback before saving. Syntax validation does not confirm source accuracy or freshness of a stored result. If an input changes, check the resulting value before export instead of assuming it has recalculated. Common mistakes:
  • Use double braces around an attribute key: {{msrp}}, not msrp.
  • Check that the referenced keys exist and their values use the expected type and units.
  • Test each part of a complicated expression before combining it.
  • Compare stored results with sample inputs before export. Do not assume that a syntactically valid formula or an old stored result is accurate.
When NOT to use Formula:
  • When the answer depends on interpreting product context or supplier prose
  • When the rule is subjective rather than repeatable
  • When you need to generate a description or make an evidence-based judgment
The Formula functions reference includes a complete measurement-extraction walkthrough, expected results and blank-result troubleshooting.
What it is: Pulls data from another entity. Non-AI relational lookup. You specify which entity to look up and which field to return. Best for:
  • Fetching vendor information for each product
  • Pulling category details for bulk assignment
  • Looking up pricing policies from a master table
  • Retrieving related data from other entities without duplicating it
AI Support: No. This is a pure relational lookup. How it works:
  1. You configure which entity to look up (e.g., Vendors)
  2. You configure which field to return (e.g., “Pricing Policy”)
  3. When the attribute is accessed, it queries the related entity and returns the value
  4. If the related record changes, the lookup reflects the update
Configuration:
  • Attribute Selector: Choose the entity to look up (e.g., Vendors, Categories)
  • Column Selector: Choose which field from that entity to return (e.g., “Pricing Policy”, “Lead Time”, “Commission Rate”)
  • The lookup happens automatically based on the relationship between entities
Example use cases:
  • Vendor Pricing Policy — For each product, look up its vendor and return the vendor’s “Pricing Policy”
  • Category Commission Rate — For each product, look up its category and return the category’s “Commission Rate”
  • Brand Guidelines — For each product, look up its brand and return the brand’s “Design Guidelines” URL
Validation:
  • The lookup is validated at save time to ensure the target entity and field exist
  • If the relationship breaks (e.g., vendor is deleted), the lookup will return null
Common mistakes:
  • Confusing Reference Lookup with Record Reference (Record Reference links to a single record; Reference Lookup fetches data from a linked record)
  • Using when a formula would be more efficient (if you’re just doing math, use Formula)
  • Not ensuring the relationship exists before creating the lookup

Collection Reference

What it is: Links a product (or other entity) to a collection of related records. Stores multiple references. Best for:
  • Linking a product to multiple collections it belongs to
  • Linking a product to multiple certifications or standards
  • Linking a product to multiple vendors (if it’s sold by more than one)
  • Any one-to-many relationship
Configuration:
  • Specify the related entity type (e.g., Collections, Certifications)
  • Manage which records are linked
  • View all linked records
Common use cases:
  • Product Collections — Link each product to the collections it belongs to (a product might be in “Summer Sale”, “New Arrivals”, and “Bestsellers”)
  • Certifications — Link products to certifications they hold (e.g., Fair Trade, Organic, B Corp)

Record Reference (single)

What it is: Links a product to a single related record. Stores one reference. Best for:
  • Assigning a primary vendor to a product
  • Linking a product to its primary category
  • Linking a product to its brand
  • Any one-to-one relationship
Configuration:
  • Specify the related entity type (e.g., Vendors, Brands, Categories)
  • Select which record to link
  • The link is stored and can be used in formulas and lookups
Common use cases:
  • Primary Vendor — Each product has one main supplier
  • Brand — Each product belongs to one brand
  • Manufacturer — Link to the manufacturing company record

Decision Matrix: Which Field Type Should I Use?

Next Steps

You now understand each field type and when to use it. The next step is to dive deeper into advanced attribute patterns and how to build dependent attributes (where one attribute’s configuration depends on another). See Building Dependent Attributes. For hands-on guidance on creating your first attribute, return to Creating Custom Attributes. For formula function reference, see the complete Formula Functions Reference.