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Best prompts for ChatGPT for customer support using multilingual support
A set of 14 ready-to-use, practical prompts for multilingual customer support tasks: translating messages, creating localized replies, classifying sentiment/priority, generating KB articles, escalation tickets, training data, and more. Each entry includes a clear title, concise explanation, a copy-ready prompt, a realistic example, and recommended AI backends.
Claude Opus 4
GPT-5
Gemini 2.5 Pro
Claude Sonnet 4
Gemini 2.5 Flash
You're drowning in customer messages written in languages you don't speak, frantically copying and pasting into Google Translate while your response times crawl to a standstill. Your team is burning out trying to craft culturally appropriate replies, and you're constantly second-guessing whether your automated responses sound robotic or offensive in different markets. Sound familiar?
This collection of 14 battle-tested prompts transforms your multilingual customer support from chaos into a smooth-running machine. You'll get ready-to-use prompts for everything from detecting language and sentiment to creating localized knowledge base articles and training chatbots across multiple languages. Instead of spending hours crafting responses and worrying about cultural missteps, you'll deliver fast, accurate, and culturally appropriate support that actually delights customers in their native language.
1
Detect language, summarize intent, and reply in customer's language (3 tones)
You are a multilingual customer-support assistant. Input is a single customer message. 1) Detect the language and provide ISO code. 2) Provide a 1-sentence intent summary in English. 3) Identify any product names, order numbers, dates, or key facts. 4) Produce three reply drafts in the customer's original language: Formal (concise), Neutral (standard), Friendly (warm). 5) For each draft, provide an English translation. 6) Suggest 3 internal tags (comma-separated). Output in JSON with fields: language_code, intent_summary_en, key_facts, replies: [{tone, message_local, message_en}], tags. Keep all replies <= 200 words each.
Automatically detect message language, summarize intent and context in English, then produce three concise responses (formal, neutral, friendly) in the customer's original language plus an English translation and suggested tags.
2
Create multilingual canned responses for common support scenarios
You are a customer support content generator. Given a support scenario label and a list of target languages, produce 3 concise canned replies adjusted for platform use: short (SMS/notification), email (formal), and chat (friendly). For each language provide a recommended subject line (for email). Each reply must be <= 300 characters. Output as a JSON object with keys per language containing {subject, short, email, chat}. Scenario: "Password reset instructions not working". Languages: English (en), Spanish (es), French (fr), German (de), Japanese (ja).
Generate short, support-approved canned replies for a specific issue in multiple languages, with character limits and recommended subject lines.
3
Summarize a multilingual chat transcript and recommend next actions
You are a support analyst. Input: a chat transcript where messages may be in different languages. 1) Provide a 50–80 word summary in English. 2) List unresolved issues (bullet list). 3) Propose three actionable next steps with priority levels (High/Medium/Low). 4) Provide a one-message reply template in the customer's last-used language aiming to close or escalate. Output as JSON: {summary_en, unresolved_issues[], next_steps:[{action,priority,why}], reply_language_code, reply_template}.
Take a chat log with messages in multiple languages, summarize the conversation in English, list unresolved issues, propose 3 next actions with priority, and provide a reply template in the customer's language.
4
Classify sentiment, urgency, and SLA priority across languages
You are a multilingual classifier for support triage. Input is one customer message. Output a JSON object: {language_code, sentiment:[positive|neutral|negative] (score 0-1), urgency:[low|medium|high] (reason_en), sla:[Immediate|24h|72h] (justification_en)}. Use content, words like 'refund', 'broken', 'can't access', and all-caps or exclamation marks as features. Keep reason and justification to one sentence each.
Analyze a single customer message in any language and return sentiment, detected urgency level, and recommended SLA category (Immediate, 24h, 72h) with a brief justification in English.
5
Localize a knowledge-base article for a target locale and language
You are a localization expert. Input: an English KB article text and target locale code (e.g., es-ES, de-DE, ja-JP). Translate the article, adapt cultural references and examples, convert measurement units, adjust date/number formats, and append a short region-specific compliance or shipping note (if applicable). Output the localized article with a short meta-line: 'Adaptation notes' listing changes made.
Convert an English KB article into a localized version: translate, adapt examples, convert units/formats, and add region-specific legal or shipping notes.
6
Rewrite a response to comply with regional politeness and legal requirements
You are a compliance-aware copy editor. Input: draft_response (in any language), target_locale (e.g., 'fr-FR' or 'en-GB'), and issue_type (e.g., 'refund','warranty'). 1) Rewrite the reply in the same language to match local politeness norms. 2) Replace or remove any legal-risk phrases and add required local disclosure sentences. 3) Provide a 1-line explanation in English of what you changed. Output JSON: {rewritten_response, explanation_en}.
Given a draft reply and a target locale/language, rewrite to meet local politeness norms, remove risky legal phrasing, and add required disclosures (e.g., GDPR, consumer rights).
7
Generate a bilingual escalation ticket including customer context and attachment requests
You are an internal ticket generator. Input: customer messages (may be multilingual), order_id, channel, and brief agent notes. Produce: (A) Ticket title, (B) Full ticket description in English summarizing issue, steps taken, and desired outcome, (C) A 2-line bilingual summary in the customer's language followed by English, (D) Checklist of attachments to request from the customer (photos, video, logs). Output as JSON with fields: title, description_en, bilingual_summary, required_attachments[].
Create an internal escalation ticket in English and include a short bilingual summary for the regional team, plus a checklist of evidence needed from the customer.
Stop Losing Your AI Work
Tired of rewriting the same prompts, juggling ChatGPT and Claude in multiple tabs, and watching your best AI conversations disappear forever?
AI Flow Chat lets you save winning prompts to a reusable library, test all models in one workspace, and convert great chats into automated workflows that scale.
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