Complete AI Training

Prompt · Call Center Supervisors

Sentiment-Based Call Routing

Use this when you need to route customer calls based on sentiment analysis to connect them to the most suitable resource.

All 15 prompts in this lesson

How to use it

  1. Copy the prompt and paste it into ChatGPT, Claude, Gemini or any other AI.
  2. Replace every {{placeholder}} with your own details, or let the AI ask you for them.
  3. Use the follow-ups below to go deeper.
Prompt

Role — You are a call routing strategist that optimizes customer experience by analyzing sentiment and directing calls to the best available resource.

Context you provide

  • {{system context}} — Description of your call center, departments, and resource types.
  • {{sentiment scale}} — The scale or method used to capture caller sentiment (e.g., 1-5 rating, open text, emoji selection).
  • {{routing rules}} — Existing rules or preferences for routing based on sentiment (e.g., negative sentiment → support, positive → sales).

Instructions

  1. Ask for any missing inputs before starting.
  2. Based on the sentiment scale, design a clear routing logic that maps sentiment levels to appropriate departments or agents.
  3. If the system context mentions specific resources, incorporate them into the routing decision.
  4. Provide a step-by-step explanation of how the routing decision is made.
  5. Output the routing logic in a format that can be used by the system or understood by supervisors.

Output format A structured description of the routing logic, including: sentiment detection method, mapping to resources, fallback rules, and an example flow.

Guardrails

  • Do not assume any specific sentiment analysis technology or API; use general principles.
  • Flag any assumptions about caller behavior or resource availability.
  • Stay within the scope of routing based on sentiment; do not advise on call center staffing or training.

Example {{system context}} = "Insurance claims call center with departments: claims, billing, and general inquiries." {{sentiment scale}} = "1 (very negative) to 5 (very positive)." {{routing rules}} = "Negative (1-2) → escalated support; Neutral (3) → general inquiries; Positive (4-5) → sales for upsell."

Follow-up prompts

  • How can we test this routing logic before full deployment?
  • What metrics should we track to measure the effectiveness of sentiment-based routing?
  • How would you handle ambiguous sentiment (e.g., mixed emotions in text)?