What makes good source data
Enrichment writes from your existing product data and will not invent facts beyond it. Input quality sets a ceiling on output quality, so this is the first thing to check.
Enrichment amplifies, it does not fabricate
If a record says 100% cotton, enrichment can write about breathability and care. If the record never mentions material, the output won’t either — the gap stays a gap rather than being filled with a plausible guess.
That is the intended behaviour. Invented specifications are worse than missing ones.
What a workable record contains
The more of these you have, the more there is to write from.
What to fix first
- Products with no usable name. These fail or produce generic text. Fix them or exclude them.
- Placeholder text. “Description coming soon” and lorem ipsum produce exactly what you would expect.
- Your thinnest records. Sort by description length. The bottom of that list either needs a data pass first or should be left out of early batches.
- Inconsistent units and terminology. Standardising these reduces contradictions between generated fields.
Errors are usually a data problem
Products that fail to generate appear on the Errors tab, and retrying them is free. But a retry on an unchanged record produces the same result. Read a few failures before retrying — the common cause is a record too sparse to work from.
Entitlements are consumed when products are sent for enrichment, not when you apply results. A product counts once whether or not it has variants. Retrying errored products is free; re-running a product that generated successfully consumes another entitlement.
A quick audit before your first batch
Pick 20 products at random. For each, ask whether a copywriter with no product knowledge could write a useful paragraph from the record alone. If the answer is no for most of them, fix data before spending entitlements.