KB article
From KPI to Story: A Repeatable Explanation Template
A consistent template makes AI explanations easier to generate and trust.
arf-kbanalytical-explainabilityexplanation-templatedriverssegmentation
TL;DR
- Structure turns numbers into narratives.
- Templates reduce ambiguity.
The problem
- Explanations vary in quality and format.
- AI answers lack consistency.
Why it matters
- Templates help users interpret answers quickly.
- They enable evaluation and comparison.
Symptoms
- AI explanations omit drivers or time context.
- Different KPIs have different explanation styles.
Root causes
- No standard narrative format.
- Measures lack supporting metadata.
What good looks like
- KPI → change → drivers → segments → caveats.
- Consistent ordering and language.
How to fix
- Define a standard explanation template.
- Update AI prompts or outputs to follow it.
- Add required metadata fields.
Pitfalls
- Templates that are too rigid for complex cases.
- Ignoring user feedback on clarity.
Checklist
- Template defined and documented.
- AI outputs follow the template.
- Template iterated based on feedback.
Framework placement
Primary ARF layer: Analytical Explainability. Diagnostic bridge: semantic-reliability, execution-reliability, change-reliability.