Mortanas AcademyHow do you implement Design AI Output Contracts?
Specify required fields, formats, uncertainty labels and failure behaviour so outputs can be validated before use.
Design reliable instructions with bounded context, output contracts, evaluations and version control.
Implementation in six steps
State which person, decision or task this work supports. Record the consequence of an incorrect output and name the accountable owner.
Specify required fields, formats, uncertainty labels and failure behaviour so outputs can be validated before use. Add a source, scope and review date to used information; separate assumptions from confirmed facts.
Start with low-risk, reversible examples. Include normal, ambiguous, missing-data and exception cases in the pilot.
Define explicit approval points for material claims, external messages, personal data, prices, publishing, payments or difficult-to-reverse changes.
Create a machine-checkable output specification after the pilot. Retain the instructions, evidence, decision, corrections and owner in a traceable record.
Track schema pass rate and missing-field accuracy as the primary measure. Confirm that faster or greater output does not hide lower quality, higher correction burden or new risk.
Worked example
In a worked pilot, the team selects five representative tasks and two exception cases. Each case uses the same evidence pack, receives human review and records corrections. The team completes a machine-checkable output specification. It proceeds to wider use only when schema pass rate and missing-field accuracy and the agreed quality and risk guardrails are met.
Working output
A machine-checkable output specification
Primary measure
Schema pass rate and missing-field accuracy
Measurement, quality and risk
Signals to track together
- schema pass rate and missing-field accuracy
- Human correction time and number of changes
- Exception-routing accuracy
- Source and decision traceability
- User or customer impact
Risks to monitor
- Plausible but incorrect output without source or context
- Use of personal, confidential or contractual data without permission
- Hidden uncertainty or exceptions inside an automated decision
- Higher human correction burden despite faster generation
- Missed differences in region, sector or version
Language, region and scope note
Mortanas Academy publishes English and Turkish learning resources for audiences in Türkiye and the United Kingdom. Language selection does not determine legal jurisdiction. Current local rules and qualified review should be checked for personal data, direct marketing, consumer, employment, security or sector-specific obligations.
Sources and verification
Check the current version of primary sources and your implementation context. Commercial outcomes are not guaranteed.
Frequently asked questions
How do you implement Design AI Output Contracts?
Specify required fields, formats, uncertainty labels and failure behaviour so outputs can be validated before use.
What working output should be created?
Create a machine-checkable output specification with a named owner and review date so implementation remains traceable.
How should success be measured?
Use schema pass rate and missing-field accuracy as the primary measure, together with quality, correction burden and risk signals.
Does this guide replace professional advice?
No. It is educational; legal, security, financial, medical or regulated decisions require appropriately qualified review.