Output risk stratification
Classify model outputs by consequence: low-risk outputs that can be deployed without review, medium-risk outputs that require spot-checking, and high-risk outputs that require human sign-off before action.
Method
Five-step validation loop design
A validation loop is not a quality assurance checkpoint bolted onto the end of a workflow. It is a structural feature of how the workflow is designed — built to collect signal, correct errors, and feed learning back into the system.
Review workflow design
Define the human review process for each output tier: who reviews, what they are reviewing for, what override and correction actions are available, and how decisions are recorded.
Exception handling path
Design the workflow path for outputs that fail review: routing logic, escalation destination, fallback to manual processing, and documentation requirements for audit and compliance.
Feedback capture mechanism
Build structured feedback collection into the review interface: correction type, severity, root cause tag, and reviewer annotation. This data drives retraining and prompt improvement cycles.
Loop closure and improvement cadence
Define the cadence for reviewing accumulated feedback: weekly triage, monthly improvement sprint, and quarterly model update cycle. Assign ownership for acting on feedback data.
Outputs
Artifacts produced by the process
Output risk classification matrix
Documented risk tiers for all model outputs with review requirements per tier.
- Output type and consequence classification
- Review requirement by tier
- Sampling rate for spot-check tier
Review workflow specification
Detailed design of the human review process including UI, decision options, and audit requirements.
- Reviewer role and access definition
- Accept, correct, and override actions
- Review time SLA per output tier
Exception routing map
Decision tree for failed outputs: escalation path, manual fallback, and compliance documentation.
- Failure type to escalation destination
- Manual process fallback specification
- Audit trail and record-keeping requirements
Feedback schema and cadence protocol
Structured feedback data model and improvement review schedule.
- Feedback fields and taxonomy
- Aggregation and triage schedule
- Improvement action ownership
Engagement Cadence
How the process runs in practice
Typical timeline: 2-3 weeks (design); ongoing in production
- Week 1: output risk stratification and review workflow design
- Week 2: exception handling design and feedback capture mechanism
- Week 3: improvement cadence definition, tooling configuration, and launch
Output: a production validation system that catches failures early, collects structured improvement signal, and creates a continuous learning loop for the AI system.