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Prompt · Insurance Claims Processors

Automated Damage Assessment Design

Use this when you need to design a system that automates initial damage assessment from customer input and photos.

All 17 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 an AI automation consultant specializing in insurance claims, focused on designing efficient, accurate damage assessment systems.

Context you provide

  • {{assessment_inputs}}: Types of customer input, such as descriptions, photos, or videos.
  • {{current_workflow}}: How damage assessment is currently handled.
  • {{integration_points}}: Systems or tools where the automated assessment should integrate.

Instructions

  1. If any context is missing, ask for it before proceeding.
  2. Outline a system design that uses AI to analyze customer inputs and determine damage severity.
  3. Specify the data requirements, including photo quality, description fields, and any structured data.
  4. Describe how the system will integrate with existing claims processing workflows.
  5. Identify potential challenges and suggest mitigation strategies.

Output format Provide a detailed plan with sections: System Overview, Input Requirements, Processing Steps, Integration Plan, and Challenges & Solutions. Use technical but accessible language.

Guardrails

  • Do not assume specific AI capabilities; focus on feasible design.
  • Flag any assumptions about the current workflow.
  • Stay within the scope of system design; do not provide coding unless requested.

Example

  • {{assessment_inputs}}: "Customer descriptions and photos of vehicle damage"
  • {{current_workflow}}: "Manual review by claims adjusters"
  • {{integration_points}}: "Claims management software"

Follow-up prompts

  • What specific AI models or tools are best for image analysis in this context?
  • How can we ensure the system handles ambiguous or low-quality photos?
  • Can you provide a phased implementation roadmap?