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Evaluation Fundamentals: Why You Need an Eval Set
Why evals matter, golden sets, regression.
What You Will Learn
- Understand why evals matter.
- Build an eval mindset.
- Define golden sets.
- Track regressions.
- Plan eval adoption.
Why This Matters
Without evals, you can't measure if Copilot is improving or regressing. Evals turn 'feels good' into 'passes 8/10 of our tasks'.
Concept Explained
An eval set is a fixed set of tasks with expected outputs. Run Copilot on the set; score outputs. Track over time to detect regressions when models or prompts change.
How It Works
Build a golden set (10–50 tasks). Define scoring rubric. Run periodically. Track scores. Alert on regressions.
Step-by-Step Tutorial
1. Understand why
Without evals, you can't measure quality objectively.2. Build golden set
10–50 representative tasks (see IN-34 for starter).3. Define rubric
Correctness, completeness, idiomaticity (0–5 each).4. Run periodically
Monthly, or when prompts/models change.5. Track and alert
Score over time. Alert on regressions.Real-World Example
A team built a 20-task eval set. Baseline: 78%. After model upgrade: 82%. After prompt template change: 75% — regression caught. Rolled back template. Eval saved them from shipping a regression.
Example Prompts / Commands / Code
"""Golden Set: 20 tasks
Tasks:
1. Refactor 50-line function (TypeScript)
2. Generate tests for parser (Python)
3. Add JSDoc to auth module
4. Convert callbacks to async/await
5. Fix null pointer bug
6. Explain regex
7. Generate SQL query
8. Write a Playwright test
... (20 total)
Rubric per task:
- Correctness (0-5): does it work?
- Completeness (0-5): all edge cases?
- Idiomaticity (0-5): matches conventions?
Score per task: 0-15
Total: 0-300
Baseline: 234/300 (78%)
After model upgrade: 246/300 (82%)
After prompt change: 225/300 (75%) ← regression!
"""Common Mistakes
- No eval set — can't measure quality.
- Tasks not representative — eval doesn't reflect real work.
- Rubric too vague — subjective scores.
- Not tracking over time — regressions slip in.
Best Practices
- Build a golden set of 10–50 tasks.
- Define a clear rubric (0–5 per dimension).
- Run monthly, or when prompts/models change.
- Track scores over time.
- Alert on regressions (>5% drop).
Troubleshooting
| Problem | How to Fix |
|---|---|
| Eval takes too long | Reduce to 10 tasks. Or automate scoring. |
| Scores are subjective | Refine rubric. Add examples for each score level. |
Practical Exercise
Your Turn
Plan an eval set for your team. Pick 10 representative tasks. Define a rubric. Run baseline.
Professional Challenge
Build a 20-task eval set. Run monthly. Track scores for a quarter. Document trends.
Key Takeaways
- Evals turn 'feels good' into 'passes X/Y tasks'.
- Golden set: 10–50 representative tasks.
- Rubric: correctness, completeness, idiomaticity (0–5).
- Run monthly; track over time.
- Alert on regressions (>5% drop).
Frequently Asked Questions
Should every team have an eval set?
Automate scoring?
Further Reading
Official References
SEO Metadata
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Categories: AI Tools, GitHub Copilot
Tags: GitHub Copilot, Professional, Evaluation, Golden Set, Regression, IMCSEIAN, Tutorial, IMCSEIAN
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