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Quality Checkpoint: Build an Eval Set for Your Team
A small golden set + a scoring rubric.
What You Will Learn
- Build a 10-item golden set.
- Define a scoring rubric.
- Run prompts through eval.
- Track regressions.
- Iterate prompts based on results.
Why This Matters
An eval set turns 'Copilot is good enough' into 'Copilot passes 8/10 of our tasks'. It catches regressions when models or prompts change. This checkpoint builds a team eval set.
Concept Explained
A golden set is 10 representative tasks your team does. A rubric scores each output 0–5. Running prompts through the eval set gives a measurable quality score.
How It Works
Pick 10 real tasks. Write the prompt for each. Define a rubric (0–5 per dimension). Run prompts. Score outputs. Total = team quality score. Re-run when prompts or models change.
Step-by-Step Tutorial
1. Pick 10 tasks
Representative of team's actual work.2. Write prompts
Same prompts you'd use in real work.3. Define rubric
Correctness (0–5), completeness (0–5), idiomaticity (0–5).4. Run prompts
Execute each. Capture output.5. Score
Score each output per rubric.6. Track
Total score = baseline. Re-run when prompts or models change.Real-World Example
A team built a 10-task eval set. Baseline: 38/50 with GPT-5. Switched to Claude for refactoring tasks: 44/50. Documented the improvement; adopted Claude for refactors team-wide.
Example Prompts / Commands / Code
Task 1: Refactor 50-line function
Prompt: [your standard refactor prompt]
Rubric: correctness (0-5), completeness (0-5), idiomaticity (0-5)
Score: ____ / 15
Task 2: Generate tests for [function]
Prompt: [your standard test-gen prompt]
Rubric: correctness, completeness, coverage
Score: ____ / 15
... (10 tasks total)
Total: ____ / 150
Run date: ____ Model: ____
Dimension 0 1-2 3-4 5
----------------------------------------------------------------
Correctness Wrong Partial Mostly Fully correct
Completeness Missing Half Most All covered
Idiomaticity Wrong style Generic Acceptable Idiomatic
Common Mistakes
- Tasks not representative — eval doesn't reflect real work.
- Rubric too vague — scores are subjective.
- Not re-running when prompts change — regressions slip in.
- Not acting on results — eval without action is wasted.
Best Practices
- Pick 10 tasks representative of team's actual work.
- Define a clear rubric (0–5 per dimension).
- Run prompts; score outputs.
- Track total as baseline.
- Re-run when prompts, models, or conventions change.
Troubleshooting
| Problem | How to Fix |
|---|---|
| Scores are subjective | Refine rubric. Add examples for each score level. |
| Eval takes too long | Reduce to 5 tasks. Re-run less frequently. |
Practical Exercise
Your Turn
Build a 10-task eval set for your team. Define a rubric. Run prompts. Score. Document as baseline.
Professional Challenge
Re-run the eval set when a new model ships or your prompt templates change. Track regressions over time.
Key Takeaways
- Eval set = 10 representative tasks.
- Rubric: correctness, completeness, idiomaticity (0–5 each).
- Run prompts; score outputs.
- Total = baseline quality score.
- Re-run when prompts or models change.
Frequently Asked Questions
How often to re-run?
Should eval be automated?
Further Reading
Official References
SEO Metadata
SEO title: Quality Checkpoint: Build an Eval Set for Your Team
Meta description: A small golden set + a scoring rubric.
Primary keyword: quality checkpoint
Secondary keywords: quality checkpoint: build an eval set for your team
Search intent: Informational
URL slug: /quality-checkpoint-build-eval-set-team
Categories: AI Tools, GitHub Copilot
Tags: GitHub Copilot, Intermediate, Checkpoint, Eval Set, Quality, IMCSEIAN, Tutorial, IMCSEIAN
Featured image concept: IMCSEIAN lesson card for Quality Checkpoint: Build an Eval Set for Your Team
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