AI STRATEGY
Establish AI Quality Standards
Know What’s Safe Enough to Ship
Thresholds and zones of safety determine when AI outputs are good enough to release—or risky enough to escalate. They make your evaluation actionable and help with continuous improvement.
Why it's Important
Sets a clear bar for releasing features
Create LLM-as-Judge for automated testing and gating
Reduces risk of harmful or inaccurate outputs
Clarifies expectations for teams and users
Enables performance alerts and drift tracking
How to Implement
Define minimum acceptable score for each quality metric
Group scores into zones (e.g., green/yellow/red)
Align thresholds with use case severity (e.g., medical vs. chatbot)
Document when to escalate to human review
Build thresholds into testing and CI pipelines
Communicate thresholds to stakeholders and annotators
Include thresholds in acceptance criteria for features
Available Workshops
Threshold Setting Scenarios
Red Team Testing Workshop
Risk Severity Calibration Session
Human-in-the-Loop Role Simulation
Output Escalation Drill
Acceptance Criteria Sprint Planning
Deliverables
Threshold matrix by output type
Human review escalation rules
Risk tiering by product feature
Release gating checklist
QA/playbook documentation
How to Measure
% of outputs above threshold at release
Number of threshold violations over time
Time to resolve escalated outputs
Escalation volume by category
False positive/negative rates in escalation
Time-to-review high-risk outputs
Threshold changes tracked per model version
Pro Tips
Use color zones (red/yellow/green) to guide reviewer action
Add thresholds to CI/CD pipelines to catch issues pre-release
Include a rationale for each threshold in documentation
Allow for “buffer” zones to handle borderline cases
Share threshold violations in retros or OKR updates
Get It Right
Calibrate thresholds based on real user behavior
Tailor thresholds to product tiers or user groups
Make thresholds auditable and updateable
Set conservative thresholds at MVP stage
Communicate zones visually in dashboards
Don't Make These Mistakes
Setting thresholds without validating with real data
Using static thresholds in evolving systems
Failing to define who owns escalations
Relying only on quantitative metrics
Ignoring false negatives (i.e., unsafe outputs that slip through)