Suppose you have to generate a multilingual support system using a language model. How would you design prompts to handle different languages effectively?

I'd keep one well-tested set of instructions, tell the model which language to answer in rather than leaving it to guess, ground answers in support content that exists in each language (or can be translated reliably), and test every supported language with native speakers, because quality varies between languages.

1. Detect and set the language Use the customer's chosen language from their account or the interface, or detect it from the message. Then say it explicitly in the system prompt: "Reply in {language}, whatever language the help articles are in." Without this, models sometimes answer in the language of the retrieved documents rather than the customer's.

2. One master prompt Write the instructions once, usually in English, and add the target language as a variable. Modern models follow English instructions well while replying in another language, and one prompt is far easier to maintain than a dozen translations. Translate the prompt only if tests show it helps for a specific language.

3. Knowledge in the right language

• Retrieve help articles in the customer's language when they exist.
• Use a multilingual embedding model, so a question in Spanish can find an English article.
• When only English content exists, let the model answer in the customer's language from the English source, and test that terms are translated correctly.

4. Terminology and locale

• A glossary per language for product names, features and terms that must not be translated, or must be translated one particular way.
• Dates, numbers, currencies, addresses and units in local formats.
• The right formality, such as "tu" or "vous" in French, or polite forms in Japanese, set per language.

5. Examples per language where it matters If tone or formality goes wrong in one language, add an example in that language.

6. Mixed and unclear cases Customers switch languages mid-conversation or mix them. Decide the rule (follow the latest message, or the account setting) and state it. For very short or unclear messages, keep the conversation's language.

7. Escalation Hand over to agents who speak the language, with a summary in the agent's language.

8. Test every language

• Model performance in less widely used languages is usually somewhat lower than in English; makers publish per-language results.
• Build a test set per language, including slang, typos and mixed language, and have native speakers review samples for accuracy, tone and cultural fit.
• Monitor ratings and escalation rates by language after launch.

9. Cost and length Many languages use more tokens than English for the same content, so check context and cost budgets per language.