How cache keys and validators make LLM product catalog translation production-ready

Building a reliable LLM pipeline for translating product catalogs requires more than prompt engineering — two core mechanisms separate a working demo from a production system. Operating on granular (product_id, field, target_lang) triples instead of whole product blobs allows precise cache invalidation and field-level review tracking. Cache keys must include every output-influencing variable — source text, model version, prompt version, and glossary version — to prevent stale or mixed-generation translations from silently entering the catalog. Text normalization before caching is equally critical, as scrapers often introduce byte-level differences like non-breaking spaces that cause unnecessary re-translation of semantically unchanged content. Finally, a validator that checks the model's output against source invariants — such as preserved placeholders, numeric values, and HTML tags — catches silent errors that would otherwise surface as incorrect product specifications.