One video has to serve several language markets, and literal translation would drift from the original claims and timing. In this case, the immediate task is to localize a product video script and captions. The workflow should produce per-language scripts and caption files whose claims, units and timing match the source footage and the merchant's records.
First replace every [bracketed] placeholder with real store scope and constraints, then add any actual records or data you have, and paste it into your preferred AI assistant or Spark agent. Spark will follow this page's workflow to produce structured output and verification steps.
Act as a Shopify operations specialist.
Help me localize a product video script and captions.
My store context is [describe the business, products and target audience].
My scope is [specify the affected products, pages, customers or reporting period]. Use these inputs: [the source script with timecoded caption segments, target languages with reading-speed norms, the cross-language glossary of consistent terms, and product records covering units, materials and claims per market]. Adapt each language's narration and captions into natural phrasing while carrying every claim, unit and timecode unchanged from the source, marking any segment that cannot fit its caption duration at that language's reading speed for a trim decision instead of silently shortening it.
Return localized scripts and caption files, then verify them against the source script and product records for the specified task.
Show the relevant evidence or before-and-after examples and explain how I can verify the result.
If essential data is missing, ask for it and mark unsupported conclusions as unknown.
Do not invent metrics, product claims or customer facts.
For any publication, message, account or store change, use only connected tools within my explicitly authorized scope and report success only after verification; otherwise provide the draft or proposed steps.Split the work into input, judgment, and deliverable phases. Verify each step's output before moving to the next.
Collect the source script with its timecoded caption segments, the target languages with their reading-speed norms, the glossary of terms that must stay consistent across languages, and the product records covering units, materials and claims for each market.
Adapt each language's narration and captions into natural phrasing while carrying every claim, unit and timecode unchanged from the source, marking any segment that cannot fit its caption duration at that language's reading speed for a trim decision instead of silently shortening it.
Deliver one script and caption file pair per language, verify each pair against the source script claim by claim and unit by unit, and confirm the caption timecodes still match the footage, noting every segment that was trimmed.
Cover decision rules, frequent misunderstandings, and required inputs so the team aligns on prerequisites before execution.
The segment is marked for a trim decision rather than silently shortened. Cutting words to fit can drop a claim or a unit, which turns a translation issue into a factual change. Trimming is allowed only where the merchant confirms the meaning survives intact.
Convert only where the product record states the unit used in that market, and let the localized number match that record. A conversion changes the stated fact, not just its phrasing. Where the record gives no market-specific unit, the source unit stays.
Glossary terms and Latin technical identifiers keep their exact form in every language version. Localized synonyms fragment brand and SKU references across markets. Any new synonym must be added to the glossary before it is used.
Start with scenarios under the same topic, then expand into cross-topic matches suggested by similarity.
Ciwi Spark builds Shopify-native AI tools and agents around the workflows merchants actually run: research, ads, translation, and customer support. Stop rewriting the same prompts by hand.