Use case 01
You want a complete XAI workflow rather than a single explainer.
Data ScientistExplainabilityAdvancedChain
Full XAI Chain AI Prompt
This prompt runs a full explainable AI workflow from global importance to business translation. It is useful when you want one coherent interpretability package that can support both technical validation and stakeholder communication. It also explicitly flags potentially risky or counterintuitive model behavior.
Prompt text
Step 1: Global importance — compute and plot SHAP feature importances (beeswarm). Identify the top 5 features driving predictions. Step 2: Effect direction — create SHAP dependence plots for the top 5 features. Describe the relationship between each feature and the prediction (linear, threshold, non-linear). Step 3: Interaction analysis — compute SHAP interaction values. Identify the strongest pairwise interaction and plot it as a 2D PDP. Step 4: Local explanation — generate waterfall plots for 3 representative predictions: high, low, and borderline. Step 5: Business translation — write a 1-page non-technical explanation of how the model makes decisions, using analogies and avoiding all technical terms. Step 6: Risk flagging — identify any feature effects that seem counterintuitive or potentially problematic from a fairness or business logic perspective.
When to use this prompt
Use case 02
Both technical and non-technical audiences need to be served.
Use case 03
You need interaction analysis and local examples in the same package.
Use case 04
You want to surface fairness or business-logic concerns proactively.
What the AI should return
A multi-step explainability package including global rankings, direction-of-effect plots, interaction findings, local explanations, business translation, and a risk flag section.
How to use this prompt
1
Open your data context
Load your dataset, notebook, or working environment so the AI can operate on the actual project context.
2
Copy the prompt text
Use the copy button above and paste the prompt into the AI assistant or prompt input area.
3
Review the output critically
Check whether the result matches your data, assumptions, and desired format before moving on.
4
Chain into the next prompt
Once you have the first result, continue deeper with related prompts in Explainability.
Frequently asked questions
What does the Full XAI Chain prompt do?+
It gives you a structured explainability starting point for data scientist work and helps you move faster without starting from a blank page.
Who is this prompt for?+
It is designed for data scientist workflows and marked as advanced, so it works well as a guided starting point for that level of experience.
What type of prompt is this?+
Full XAI Chain is a chain. You can copy it as-is, adapt it, or use it as one step inside a larger workflow.
Can I use this outside MLJAR Studio?+
Yes. The prompt text works in other AI tools too, but MLJAR Studio is the best fit when you want local execution, visible Python code, and reusable notebooks.
What should I open next?+
Natural next steps from here are Counterfactual Explanations, Decision Tree Proxy, Feature Importance.