Running a product catalog through an LLM is the easy 10% of localization.
The expensive part starts after the first pass.
Most teams treat catalog translation as a batch job: dump the SKUs, call the model, load the results. It works once. Then merchandising edits 400 titles, a supplier renames a category, and someone asks why last month's fix disappeared.
What makes an LLM content pipeline survive production is not prompt quality. It is bookkeeping:
- a content hash per field, so you only pay for what actually changed
- a human override layer that re-runs are not allowed to overwrite
- a review queue where the diff is visible before it reaches the storefront
- versioning, so a bad batch rolls back without a database restore
We build these pipelines for cross-border catalogs, and the same lesson keeps repeating: the model is a component, the pipeline is the product. Teams that skip the state tracking re-translate everything on every run — the most expensive option and the least trustworthy one, because nobody can tell which strings a human already corrected.
If you push LLM output onto a live product surface, how do you handle human edits — do your re-runs respect them, or does someone re-apply the same correction every month?