THE SHORT ANSWER
Map Merchant Center-style source fields to the current OpenAI Stable product specification by meaning. Common starting points include id to item_id, link to url and image_link to image_url. Validate values and merchant context separately; a renamed column is not a complete integration.
Write a mapping contract before converting rows
For every target field, record the source column, any transformation and the behavior when data is absent. Distinguish a direct copy from a merchant-provided default and from an approved descriptive change. This small contract makes future refreshes easier to explain.
Start with a few real variants. Check that IDs remain text, links identify the correct selection and images show the same item. Preserve the source row in the review record so a downstream finding can be traced back without reconstructing a spreadsheet by hand.
Resolve naming differences explicitly
OpenAI’s Stable discovery schema uses its own field names and value conventions. A Merchant Center-style source can provide much of the underlying information, but a value that is familiar in one source may need a deliberate target mapping. Availability is a common place to check carefully.
Treat seller context and brand as separate facts. The store operating the feed may not be the brand of every item it sells. Do not use a convenient hostname or a generic placeholder to satisfy a required field without confirming that it represents the actual seller or product.
Keep transformations narrow
Normalize whitespace or serialize a value where the target contract requires it, but do not silently change the payable amount, selected variant or meaning of a description. A mapping preview should make these transformations visible before a release is prepared.
For price and currency, use the actual source offer and the target’s money representation. For optional descriptive fields, prefer supported facts over guesses. A missing value may require a supplier request or merchant default rather than a more aggressive conversion rule.
Test the derivative independently
Validate the proposed output against the current target requirements and compare it with the original source. Include missing values, duplicate IDs, sale cases and variant groups. Keep a record of the source snapshot, mapping version and findings.
Finally, separate output preparation from delivery and acceptance. An exported artifact can be useful while platform onboarding is still pending. Make that status visible to everyone evaluating the workflow, particularly when a commercial proposal uses broad language such as ready for ChatGPT.
ILLUSTRATIVE WORKED EXAMPLE
Make it concrete.
Illustrative row: source id SHOE-0042 stays a string in item_id; link becomes url; image_link becomes image_url. The original brand remains the manufacturer’s name. A confirmed seller name is added as merchant context. A missing material value remains absent until evidence is available.
Your practical checklist
- Document copy, default and transformation rules.
- Preserve string IDs and variant selection.
- Confirm brand and seller independently.
- Preview changed commercial values.
- Keep delivery and acceptance outside the local validation result.
Common questions
Can I map fields just by matching their names?
Use meaning and scope first. Similar names can hide differences in units, allowed values or which entity the field describes.
Can a mapping invent a missing brand?
No. Obtain an authoritative product fact or resolve the source-data gap with the merchant.
Sources & scope
Official references checked 29 October 2026. Examples are illustrative, not customer results. Platform requirements can change; use current destination documentation for implementation decisions.
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