This recipe shows how to make policy information more accessible without losing precision.
Policy and legal text is hard to understand even in a user’s native language, and even harder across languages. At the same time, regulated organizations must keep wording accurate and consistent. A multilingual policy explainer provides conversational access to these texts without changing their meaning.
In general, it is always best to train the AI with documents that are written in the same language. However, its response depends on the preferred language of the end user. You can set the languages that you would like the AI to respond with in the Unless dashboard. It will then offer any of these languages if the user wishes to switch.
However, for legal policies, this may not be sufficient. Terminology needs to be consistent. The simplest way to define terminology access allowed languages is to use a translation corpus for specific lingo. However, this only works to keep terms consistent, but as the legal context may be entirely different in other countries or language areas, you may need to do more.
Create an AI Skill for policy Q&A with variables such as language, region, policy_topic, and question. You can infer language from the UI or browser and set region based on profile data or an Audience, while the skill detects policy_topic and asks for clarification only when needed.
Ground answers with flat Topics like Policies-original, Policies-translated, Policy-changes, and Data-handling. Associate each policy version and translation with the right Topic and metadata, so the skill can pull from the correct language and region combination. When a translation is missing, it can fall back gracefully to the main language but should say so explicitly.
The skill should prefer plain-language explanations but can quote short excerpts verbatim where needed. It should avoid inventing obligations or promises beyond the policy text. For complex questions, it can provide a summary and then point to the exact section and translation that governs the issue.
Internally, you can collect signals on which policy topics generate the most questions per language. The assistant can create tasks or periodic notifications for legal and communications teams summarizing common areas of confusion. This feedback can guide future clarifications and translations.
For customers, audience segmentation by language and region lets you show in-app notifications when policies change for their segment. A banner can invite them to “Ask questions about the new policy in your language,” linking directly to the explainer skill. This keeps outreach targeted and avoids overwhelming unaffected users.
Conclusion
A multilingual policy and change communication explainer makes complex rules more accessible and trustworthy across languages. With an AI Skill, flat Topics tying back to canonical texts, and internal tasks and notifications, it supports better understanding without compromising legal accuracy.
