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Telecom Prompt Old

GPTClaudeDeepSeek··468 copies·updated 2026-07-14
telecom-prompt-old.prompt
Analyze the following telecom customer service call and extract:

1. Classification of the issue: Billing, Connectivity, Retention, General Inquiry, Cancel.
2. Core issue reported by the customer.
3. Specific resolution steps taken by the agent (e.g., diagnostics, refunds, technician scheduling) in only a few words.
4. Degree of customer satisfaction at the end (choose: Satisfied, Dissatisfied, Angry, Frustrated, Neutral).
5. Follow-up offered or promised: (Yes/No, with brief details in only a few words).
6. List any personally identifiable information (PII) mentioned (e.g., phone number, email, account ID, customer name).
7. Summarize the emotional tone of the customer throughout the call using natural language. Use phrases like:
   - "Calm and cooperative throughout"
   - "Initially upset, became satisfied after support"
   - "Started cordial but became increasingly frustrated"
8. Based on that emotional state of the customer, recommend a logical resolution tactic that a supervisor could do for the customer to retain their business using natural language in only a few words.
9. Transcript for a supervisor to follow-up with the customer and de-escalate the situation (this should be based on the recommended resolution tactic).

Note:
- Emotional tone should be based on clear cues in customer language.
- Tactic must reflect behavioral patterns and emotional state of the customer, not just issue type.
- Use behavioral reasoning. If sentiment progression doesn't fit known patterns, describe the tone and suggest best-fit tactic based on customer affect and urgency.

when to use it

Community prompt sourced from the open-source GitHub repo ReverendBayes/AI-Powered-Call-Center-Intelligence (MIT). A "Telecom Prompt Old" style prompt — adapt the placeholders and specifics to your task. Imported as-is and not independently retested here, so check the output before relying on it.

tags

productivitycommunitydeveloper

source

ReverendBayes/AI-Powered-Call-Center-Intelligence · MIT