Feature Importance in Plain English AI Prompt

My model gave me a feature importance chart. Help me understand what it means and what to do with it.

Feature importance output: {{feature_importance_output}}
Model predicts: {{target_variable}}

1. What feature importance means — in plain English:
   - Explain what feature importance is measuring: not 'which columns correlate with the target', but 'which columns the model actually relies on most to make its predictions'
   - Why does this matter? Because it tells us what the model believes drives the outcome

2. Walk through the top features:
   For each of the top 5 most important features:
   - Name: what is this column and what does it measure?
   - Direction: when this column has a high value, does the model predict a higher or lower outcome?
   - Business interpretation: what does this mean in business terms?
   - Does this make intuitive sense? If a feature ranks highly but you cannot explain why it matters, that is a warning sign.

3. Red flags to look for:
   - Is any feature suspiciously important? (e.g. a unique identifier like customer_id should not be important — it means the model memorized the training data)
   - Is any feature important that could not realistically be known at prediction time?
   - Is any feature important because it is a proxy for something else you should be measuring directly?

4. What is missing:
   - Are there features you expected to be important that are near the bottom? Why might the model not be using them?
   - Could an important column be missing from the data entirely?

5. What to do with this information:
   - Which features could I collect or engineer more of to improve the model?
   - Is there a feature so dominant that the model might be 'cheating'?
   - What does this feature importance tell us about the business problem — independent of the model?