AI Readiness & Interoperability
Semantic Contracts: Setting Expectations for Questions and Answers
Semantic contracts define what questions are valid and how answers should be interpreted.
Rendering diagram...
A decision flow validating questions against a contract.
TL;DR
- • Contracts reduce ambiguity and misunderstanding.
- • They define scope, units, and acceptable queries.
The problem (layman)
- • Users ask questions outside the model’s intended scope.
- • AI answers without clear constraints.
Why it matters
- • Contracts prevent misuse and reduce errors.
- • They make evaluation possible.
Symptoms
- • AI answers unsupported questions.
- • Stakeholders misinterpret results.
Root causes
- • No explicit scope for metrics.
- • Lack of documentation for valid queries.
What good looks like
- • Defined set of valid questions per KPI.
- • Clear scope, units, and exclusions.
How to fix (steps)
- • Create semantic contracts for top KPIs.
- • Publish and enforce valid question patterns.
- • Tie contracts to metadata.
Pitfalls
- • Contracts too broad to be useful.
- • No enforcement or education.
Checklist
- • Contracts documented.
- • Valid question list published.
- • Contracts reviewed with stakeholders.