Model Prediction Explainer AI Prompt

My model made a prediction for a specific case. Help me explain to a business stakeholder why the model predicted what it did.

Case details: {{case_details}}
Model prediction: {{prediction}}
Model explanation output (SHAP or similar): {{explanation_output}}

1. What did the model predict and how confident is it:
   - State the prediction in plain English: 'The model predicts that [outcome] with [confidence]%'
   - Put the confidence in context: is 72% confidence high or low for this type of problem?

2. Why did the model predict this:
   - Using the explanation data, describe in plain English the top 3 reasons the model made this prediction
   - Format each reason as: '[Feature name] = [value] pushed the prediction [up/down] because [plain English reason]'
   - Avoid technical terms. Say 'the customer has been inactive for 90 days which increased the churn risk' not 'the days_since_last_purchase feature had a positive SHAP value'

3. What would change the prediction:
   - If the business wants to change this outcome, which factors could realistically be changed?
   - Example: 'If the customer made one purchase in the next 30 days, the churn risk would likely drop from 78% to around 45%'

4. Should we trust this specific prediction:
   - Is this customer/case similar to the training data? Or is it an unusual case where the model may be less reliable?
   - Are any of the input values unusual or possibly wrong?

5. How to communicate this to the business:
   - Write a 2-sentence explanation of this prediction that a sales manager or account manager could understand and use to take action