LLM API for multilingual chatbots

Multilingual chatbots in a production application

A travel product supports English, Japanese, Spanish, and Chinese. The system detects the conversation locale, loads that locale's product glossary and policy snippets, and asks the model to reply without translating protected product names.

Quality is scored separately by language. A language does not launch because the average score is good; it launches only after native reviewers accept intent, terminology, tone, and refusal behavior for that locale.

How the multilingual chatbots workflow operates

• Detect language and locale separately.
• Load the locale glossary and approved policy snippets.
• Generate directly in the target language.
• Check protected terms, numbers, and policy wording.
• Route unsupported or low-confidence language to a human.

Call gemini-2.5-flash through LLMFly AI

Send the request from your server. Replace the example content and placeholder tool schema with data and tools from your application.

curl https://app.llmfly.ai/v1/chat/completions \
  -H "Authorization: Bearer $LLMFLY_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gemini-2.5-flash",
    "messages": [
      {"role": "system", "content": "Reply in the detected locale. Preserve GLOSSARY terms exactly. Use only the supplied locale policy and do not translate protected product names."},
      {"role": "user", "content": "Explain the cancellation window in the customer's language using the locale policy."}
    ]
  }'

Why gemini-2.5-flash is the primary model

Gemini 2.5 Flash is the first choice for a multilingual, latency-sensitive chat workload.

Use gpt-5.4-mini only for languages where it passes the same native review. Send policy-heavy or mixed-language failures to Terra.

Acceptance checks for multilingual chatbots

Failures to handle before deployment

• Assuming English predicts every language
• Machine-translating the evaluation set
• Ignoring locale policy
• Averaging away weak languages

Returned output and run records

A locale-correct reply with protected terms preserved and no policy content imported from another region.

Record the model ID, request ID, token usage, retries, validation result, and final disposition for every production run.