Multilingual assistants are not just a translation problem. They are a product and quality problem: tone changes across languages, safety policies translate poorly, and evaluation is harder because ground truth is less obvious.
Decide what "multilingual" means for your product
There are different levels of multilingual support:
- UI language only. The interface is translated, but the assistant answers in one language.
- Answer language selection. The assistant can answer in a chosen language, with consistent structure.
- True multilingual workflows. Retrieval, citations and tool actions work across languages and regions.
Localise tone and boundaries, not just words
Policies and disclaimers often translate badly. Ensure safety boundaries remain consistent and culturally appropriate. Keep user transparency patterns consistent across languages (see user transparency).
Design retrieval for multilingual content
If you use RAG, multilingual introduces extra complexity:
- Sources may exist in multiple languages with inconsistent freshness.
- Metadata and taxonomy should capture language and region (see metadata strategy).
- Permissions and tenancy must still be enforced (see RAG permissions).
Evaluate per language, not just overall
Quality can look fine overall while failing in one language. Use:
- Language-specific test sets. Include common intents and edge cases (see evaluation datasets).
- Rubrics with anchors. Especially for tone, helpfulness and safety (see evaluation rubrics).
- Regression suites. Prompt changes should not regress one language (see prompt regression testing).
Monitor outcomes by language and region
Instrument adoption and outcomes by language and region. Watch for uneven refusal rates, higher escalations, and worse task completion in specific locales (see usage analytics and fairness monitoring).
Plan for region constraints
Multilingual often overlaps with residency and regulatory constraints. Ensure routing rules and vendor configurations match region requirements (see data residency and routing).
Great multilingual assistants feel consistent: same structure, same trust signals, and the same safety boundaries in every language.