Add a target language to an existing engine with model chain, brand voice, and instructions configured in one conversation.
The workflow#
"Add Brazilian Portuguese to our engine. Use Claude Sonnet as primary, GPT-4.1 as fallback. Informal tone, 'você' not 'tu'. And make sure our product name 'Acme Hub' stays untranslated."
What happens:
- The assistant reads the engine's current configuration to understand the pattern
- Creates the model chain for
en → pt-BRwith ranked fallback - Adds a brand voice profile specifying informal register with 'você'
- Adds a glossary entry marking "Acme Hub" as non-translatable for
pt-BR - Runs a test localization to verify the configuration works
What gets configured per locale#
| Component | What to specify |
|---|---|
| Model chain | Primary model + fallback(s) for the locale pair |
| Brand voice | Register (formal/informal), pronoun choice, tone |
| Instructions | Locale-specific rules (punctuation, spacing, formatting) |
| Glossary extensions | Terms that need locale-specific localizations or protection |
Common patterns#
Locale variants#
"Add pt-BR. Copy the model chain from pt-PT but change the brand voice to informal."
The assistant reuses the existing Portuguese configuration as a starting point, adjusting only what differs for Brazil.
CJK languages#
"Add Japanese. Use Claude Sonnet — it handles Japanese well. Brand voice: polite-formal (keigo for user-facing, plain form for developer docs). Add instructions for honorific consistency."
Languages with complex register systems benefit from explicit brand voice and instruction configuration.
Right-to-left languages#
"Add Arabic. Same model chain as other languages. Add an instruction about mixed LTR/RTL content handling in UI strings."
The assistant configures the locale and adds RTL-specific instructions.
After extending#
Once the locale is live:
- Run a localization test with representative content
- Review the output against the new rules
- Monitor with Observe for scorer results on early requests