Evaluate a multilingual AI assistant on the tasks and language combinations its users actually need, including mixed-language questions and source material. Fluent wording does not prove accurate meaning, correct retrieval or safe action selection. Define supported languages and fallback behaviour before promising a uniform experience.
Let’s plan your multilingual AI tests.
Bring your workflow and the decisions you need to make. We’ll help you clarify requirements, review the trade-offs and define a practical next step.
Specify language coverage by workflow
Separate input language, source language and output language. A user asking in Hindi about an English policy is a different case from translating a short label. Include transliterated input and local terminology where they are relevant to the audience.
Select reviewers who understand both the language and business task. General fluency is not enough to judge whether a domain-specific condition survived translation.
Build a multilingual evaluation matrix
| Case | What to assess | Failure to watch for |
|---|---|---|
| Same question in supported languages | Meaning and outcome consistency | Different eligibility or policy answer |
| Mixed-language input | Task and entity recognition | Lost qualifier or name |
| Source in another language | Retrieval and faithful synthesis | Unsupported translation presented as fact |
| Numbers and dates | Meaning preserved in context | Changed unit or ambiguous date |
| Unsupported language | Honest fallback | Confident low-quality answer |
| Consequential action request | Correct authority and confirmation | Translation bypasses action restrictions |
Use naturally authored examples as well as translations. Literal translations of one English test set may miss how people actually ask questions.
Preserve critical terms and evidence
For a hypothetical customer-support assistant, product names and plan conditions may need consistent terminology across languages. Maintain an approved glossary and show source references where the workflow relies on documented evidence.
Allow an answer to explain uncertainty or retain an original term when translation would be misleading. Do not hide differences behind a single aggregate accuracy score.
Monitor language-specific failures
Report outcomes by language and task. A strong result in the highest-volume language can conceal weak performance elsewhere. Include human-review effort and unsuccessful handoffs in the assessment.
Version prompts, translation resources and model changes. Rerun relevant cases after an update; improvements in one language can coincide with regressions in another.
Multilingual AI questions
Can we claim support because the model can generate the language?
Generation capability is only a starting point. Evaluate the end-to-end task and its evidence requirements.
Should every language use an identical acceptance threshold?
Set the acceptable outcome from the business task, and investigate gaps rather than quietly lowering expectations for a language group.
Use the AI evaluation dataset framework, and discuss language coverage with Drema's AI team.