Define What "Good AI" Means for Your Product

AI outputs aren’t just about functioning—they need to be relevant, accurate, fair, and aligned with business goals. Defining quality metrics across dimensions like tone, fairness, and usability ensures your AI works in the real world.

Why it's Important

Quality varies by context—"accurate" in finance may mean something different in education.
Helps align product, engineering, and compliance teams.
Provides clarity in model evaluation and iteration.
Prevents reputational damage from biased or low-quality outputs.
Creates a foundation for scalable testing and improvement.

How to Implement

Identify 4–6 dimensions of AI quality (e.g., accuracy, relevance, tone, fairness, interpretability).
Define what success and failure look like for each.
Work with stakeholders from multiple departments (legal, design, product).
Look at existing frameworks (e.g., Responsible AI from Microsoft or Google).
Draft a clear rubric with sample outputs labeled as good/poor.
Validate rubrics with real user scenarios with manual scoring.
Revisit definitions quarterly or with each major model update.

Available Workshops

Quality Attribute Brainstorm (cross-functional team)
Output Grading Simulation (label sample outputs as a team)
AI in Context: Customer Scenario Mapping
Fairness Audit Workshop
Interpretability & Confidence Review
Role Play: Reviewer vs. User Perspective

Deliverables

List of quality dimensions and definitions
Rubric with examples and success/fail indicators
Stakeholder feedback report
Documented mapping of metrics to business goals
Version-controlled definitions (v1, v2, etc.)

How to Measure

Inter-rater agreement on rubric application (are people scoring similarly?)
Review feedback cycle times
Number of flagged misaligned outputs
Stakeholder approval or sign-off on rubric
User satisfaction changes after implementation
AI-generated content error rates
Rubric coverage across diverse content types

Pro Tips

Use example-driven rubrics—it’s easier for reviewers
Keep rubric formats consistent across teams
Incorporate qualitative and quantitative measures
Allow room for “edge cases” in your definitions
Track rubric evolution as part of product documentation

Get It Right

Involve cross-functional input from Day 1
Use real, representative outputs for scoring
Tie quality to measurable product KPIs
Make rubrics clear and repeatable
Regularly test and evolve definitions

Don't Make These Mistakes

Defining quality only as “accuracy”
Skipping user feedback when defining standards
Leaving fairness or tone out of evaluation
Using vague or overly complex rubrics
Treating quality definitions as static