KB article
Naming Measures So Humans and AI Agree
Consistent naming helps AI select the right measure and reduces ambiguity.
arf-kbsemantic-integrityunit-semanticsmeasure-inventorysemantic-annotation
TL;DR
- Names are instructions for AI.
- Explicit names reduce accidental misuse.
The problem
- Ambiguous measure names cause AI to pick the wrong calculation.
- Similar names hide different definitions.
Why it matters
- AI relies on labels to choose measures when context is unclear.
- Good names reduce the need for custom prompts.
Symptoms
- Measures named “Revenue,” “Revenue1,” “Revenue_Adj.”
- No indication of currency, time window, or exclusions.
Root causes
- No naming standard.
- Legacy measures created by different teams.
What good looks like
- Names encode metric scope, unit, and time basis.
- Deprecated measures are clearly labeled.
How to fix
- Adopt a naming convention (Metric | Scope | Unit).
- Rename measures and update report references.
- Add descriptions that expand on the name.
Pitfalls
- Renaming without updating dependent measures.
- Over‑compressing meaning into acronyms.
Checklist
- Naming standard documented and enforced.
- All key measures renamed to reflect scope and unit.
- Descriptions mirror the naming logic.
Framework placement
Primary ARF layer: Semantic Integrity. Diagnostic bridge: semantic-reliability, change-reliability.