AI STRATEGY
Create Offline Datasets for Quality Evaluation
Turn Evaluation into a Repeatable System
Automated pipelines help you track model quality continuously and at scale. They reduce manual effort, speed up validation, and allow safe, confident shipping.
Why it's Important
Supports continuous delivery of improvements
Increases speed and confidence in launches
Reduces human review overhead
Provides consistent tracking across versions
Helps catch regressions early
How to Implement
Define tests for each quality category (accuracy, safety, tone)
Automate evaluation on gold + synthetic + LLM-as-Judge sets
Schedule tests to run on each model or prompt change
Integrate with CI/CD tools and dashboards
Log results with version tags
Alert teams on threshold failures or drifts
Iterate tests as product grows
Available Workshops
Test Case Design Workshop
CI/CD Integration Sprint
Quality Dashboard UI Jam
Regression Simulation Drill
Model Versioning Strategy Review
Failure Trend RCA (Root Cause Analysis) Lab
Deliverables
Automated test suite (scripts, configs)
CI/CD pipeline config with evaluation step
Drift tracking dashboard
Alerting rules and ownership map
Version-to-version quality report
How to Measure
Test pass rate over time
Mean time to identify regressions
% of builds blocked by quality gates
Frequency of threshold violations
Regression rate by feature area
Coverage of test cases vs. total use cases
Pro Tips
Use open-source tools like LangSmith or Promptfoo
Visualize regression trends to spot model drift
Add test data from real user sessions (anonymized)
Schedule automated model evaluations pre-release
Treat pipelines as product infrastructure, not tech debt
Get It Right
Start simple: gold + synthetic + safety
Review failures weekly in standups
Add tags and version history to logs
Include human review step for fails
Add in LLM-as-Judge real time scoring
Refine test cases as new features launch
Don't Make These Mistakes
Over-engineering pipeline before use cases stabilize
Forgetting to test outputs across diverse inputs
Failing to maintain test cases over time
Ignoring results unless there’s a failure
Building dashboards no one checks