AI STRATEGY
Monitor, Adapt, and Respond Responsibly
Check the Output Like a Human Would
Even the best AI needs human oversight. Sampling and manual review of AI responses gives teams a pulse on quality, surfacing issues that metrics can’t always catch.
Why it's Important
Identifies subtle or context-sensitive failures
Supports training and reviewer calibration
Feeds qualitative insight into model tuning
Builds trust through transparency
Helps validate automated evaluation pipelines
How to Implement
Define sampling criteria (random, high risk, new features)
Set a weekly or biweekly review cadence
Use structured rubrics for scoring
Rotate reviewers and track inter-rater agreement
Store review outcomes in shared workspace
Close the loop by sharing findings with dev teams
Available Workshops
Reviewer Training Lab
Output Scoring Jam
Edge Case Deep Dive
Cross-Functional Review Sprints
Annotator Calibration Sessions
Sample-Based Triage Drill
Deliverables
Review calendar and schedule
Review templates and scoring rubrics
Annotated sample logs
Weekly review highlights report
Reviewer role and coverage tracker
How to Measure
% of reviewed samples each cycle
Reviewer agreement rate
% of samples flagged for retraining
Number of issues identified vs. missed
Time from sample to remediation
Reviewer satisfaction with tools and process
Pro Tips
Use review highlights in all-hands or retros
Include user context when scoring outputs
Let reviewers flag "unknown" for ambiguous cases
Rotate reviewers to avoid blind spots
Use review data to enrich gold test sets
Get It Right
Balance breadth (random) with depth (targeted)
Involve product, design, and support in reviews
Make review outcomes actionable
Track review fatigue and workload
Update rubrics with each major model release
Don't Make These Mistakes
Sampling only the safest or easiest outputs
Failing to record reviewer feedback
Ignoring disagreements or annotation drift
Treating review as low-priority work
Skipping communication with dev teams