Should I detect language with a library or the LLM?

A dedicated detection library is fast, cheap, and deterministic — ideal for high-volume routing. Asking the model to emit an ISO language code is simpler to wire up and handles mixed-language or short text better, but adds latency and cost. Many apps use the library first and fall back to the model on low-confidence cases.

How do I make the model reply in the user's language?

Include an explicit instruction in the system prompt such as "Reply in {language}." Passing the detected language code or name removes ambiguity. Without this, models often default to English even when the user wrote in another language.

Do I need a separate translation step?

Not for free-form responses — a capable LLM can both understand and respond in the same language in one call. You only need a dedicated translation step for fixed assets like UI labels, emails, or legal text where you want consistent, reviewable wording.

How do I handle right-to-left languages like Arabic or Hebrew?

The model handles the text fine, but your UI must set direction to RTL for those languages. Detect the language, map RTL locales to a direction attribute, and mirror layout and alignment so the rendered output reads correctly.

How do I test a multilingual app without speaking every language?

Build a fixed test set of representative inputs per target language with expected behaviours, then assert on machine-checkable properties — the response is in the right language, contains required entities, and stays within length. For fluency and tone, sample a few outputs for native-speaker review.

How to Build a Multilingual AI App

Building a multilingual AI app

A multilingual AI app lets users interact in their own language and replies in kind, across dozens of locales. Modern LLMs already understand and generate many languages, so the engineering work is mostly about routing: detect the input language, pick the right prompt, instruct the model to respond in that language, and handle locale concerns like text direction. This guide covers the pipeline, and the prototyper below shows how a detected language flows into a localised system prompt.

How the pipeline works

The flow has three stages. Detect identifies the input language — either with a fast detection library or by asking the model for an ISO code. Route uses that language to select a localised system prompt and adds an explicit “reply in {language}” instruction so the model does not drift back to English. Respond generates the answer in the target language; for fixed UI strings you may run a separate, reviewable translation pass instead of regenerating them each time.

Locale handling matters too. Right-to-left languages such as Arabic, Hebrew, Farsi, and Urdu need your UI to switch text direction, and currency, dates, and number formats should follow the user’s region rather than your default.

Tips and testing

Pass the detected language explicitly into the prompt — relying on the model to infer it from the message is unreliable for short or mixed text. Keep one base system prompt and inject the language, rather than maintaining 50 hand-written prompts. Build a small per-language test set and assert machine-checkable properties: response language, required entities, and length. For tone and fluency, sample a handful of outputs per language for native review before a wider rollout. Roll out one language family at a time so issues stay contained.