AI STRATEGY
Create Offline Datasets for Quality Evaluation
Establish a Benchmark with Gold Standard Data
A gold test set gives you a trusted foundation to evaluate your AI before release. It ensures consistency, supports regression testing, and helps quantify progress.
Why it's Important
Enables repeatable, unbiased evaluation of model performance
Identifies weaknesses before users do
Helps compare versions over time
Guides tuning and fine-tuning efforts
Builds team confidence in model quality
How to Implement
Select 50–100 real or representative user queries
Include diverse user types and edge cases
Define expected output for each query
Review examples with a cross-functional panel
Store in version-controlled format
Use gold set as a CI check before deploying new models
Update periodically as your product evolves
Available Workshops
Golden Set Drafting Jam
Real User Scenario Selection
Edge Case Identification Workshop
Review Panel Calibration
Labeling Consistency Sprint
Output vs. Expectation Gap Analysis
Deliverables
Finalized gold dataset
Annotated examples with rationale
Edge case documentation
Review panel sign-off report
Model performance baseline report
How to Measure
Model performance (e.g., accuracy, relevance) on gold set
Inter-rater agreement on gold annotations
Regression score change over time
% of test coverage by user type or feature
Average time to evaluate a new version
Number of failed checks at each release gate
Pro Tips
Label gold set outputs with multiple quality dimensions
Use version tags to track performance changes
Involve non-technical reviewers to reduce bias
Use gold sets to train new team members
Keep gold data secure and access-controlled
Get It Right
Use real-world representative queries
Calibrate expectations among reviewers
Treat gold set as a living document
Align outputs with user experience expectations
Keep it small and sharp at MVP stage
Don't Make These Mistakes
Using synthetic or unvalidated queries
Letting the gold set go stale over time
Overcomplicating annotation standards
Failing to explain rationales for expected outputs
Forgetting to benchmark with each release