Adapt training content for learners in other countries
A localization plan and adapted content that goes beyond translation to fix examples, culture, regulations, and format.
The prompt
Help me adapt a training course for learners in other countries or cultures.
Course content: [PASTE OR SUMMARIZE]
Target countries, regions, and languages: [LIST]
Learners: [ROLES, English proficiency if English is used]
Will it be translated? [yes - into what / no - English only]
Elements that must stay consistent globally: [e.g. company values, safety standards]
Check and adapt:
- Examples, names, scenarios, and images that will feel foreign or irrelevant.
- Idioms, humor, sports and pop culture references, and wordplay that will not translate.
- Units, dates, currency, number formats, and time zones.
- Local laws, regulations, and practices that differ (flag them; do not assume).
- Cultural norms that affect activities: speaking up in groups, role-plays, giving feedback to seniors, self-promotion.
- Text expansion when translated (often 20-35% longer) and right-to-left scripts that affect layout.
- Audio and video: subtitles vs. dubbing, accents.
Return:
1. A localization review table: Item | Location in course | Issue | Recommended change | Applies to which regions.
2. A rewritten version of the global master text in simplified, translation-friendly English.
3. Guidance for local reviewers: what to check and what they may change.
4. A brief for the translation vendor, including a glossary of terms not to translate.
Mark any country-specific legal or regulatory point as [CONFIRM LOCALLY]. Do not state local rules as fact.
Highlighted [brackets] are blanks to replace with your own details.
Fill in the blanks and use this promptMore Learning & Teaching prompts
What to fill in
- PASTE OR SUMMARIZE
- LIST
- ROLES, English proficiency if English is used
- yes - into what / no - English only
- e.g. company values, safety standards
- CONFIRM LOCALLY
How this prompt is built
This prompt follows the RTF structure (Role, Task, Format). RTF tells the model who to be, what to do and what shape the answer should take. It is the quickest structure that still gives noticeably better results than a bare request.
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