Define decision classes
Classify outputs by business impact, evidence requirements, policy sensitivity, and action readiness.
Deliverables
- Decision class map
- Evidence requirements
- Escalation thresholds
Guides
Reasoning quality control
A playbook for making high-stakes AI reasoning measurable, reviewable, and safe to promote.
Target outcomes
Conditions that should be true before expanding this workflow in production.
Guides
Verifier chains separate answer generation from answer approval.
Guides
Decision rubrics produce comparable quality evidence across model and prompt revisions.
Guides
Escalation policies are triggered by reasoning quality signals, not reviewer intuition alone.
Execution stages
Each stage produces operational artifacts that client teams can review and run.
Build verifier chain
Create checks for source support, policy fit, missing assumptions, contradictions, and reviewer escalation.
Deliverables
- Verifier chain template
- Structured verdict schema
- Release gate mapping
Run counterfactual suite
Evaluate reasoning behavior against case variants that alter assumptions, constraints, and evidence state.
Deliverables
- Counterfactual suite
- Failure taxonomy
- Regression threshold
Review quality telemetry
Use rubric results and verifier outcomes to decide whether to promote, revise, or block workflow changes.
Deliverables
- Decision quality rubric
- Quality dashboard
- Promotion decision log
Recommended resources
Learn references that support this playbook in delivery.
Rapports
Linked operations report
Snapshot view used to track progress and health for this playbook.
Audience and demand report
Signal view for content demand, newsletter growth, and localization pull from support requests.
- Subscriber trend vs support load.
- Top locales by request and content interaction.
- Demand signals for new playbooks and KB coverage.
Related routes