Adding a CI pass fail threshold - day 30

Written by - Millan Kaul

Scaling up - integrating our small test dataset directly into our CI/CD pipeline with a pass/fail threshold.

Don’t just fix AI bugs. Turn them into regression tests.

What I did today:

I configured a GitHub Action that runs our evaluator script on every commit. If the average accuracy score drops below 85%, the build fails.

Here is a sample GitHub Actions pipeline YAML configuration to run this check:

name: AI Evals Quality Gate
on: [push]
jobs:
  evals:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - name: Run Evals & Check Threshold
        run: |
          python run_evals.py --threshold 0.85

What surprised me:

Running evals automatically took the fear out of refactoring system prompts.

📖 This is a lesson from the 30-day AI testing framework in:

THE AI EVAL MINDSET: 30 Focused Lessons for Testing AI That Works in the Real World

by Millan Kaul

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My QA takeaway:

1. Every AI defect is a gift to your regression suite, if captured.
2. Don’t just fix AI bugs. Turn them into regression tests.

Is your AI evaluation suite running on every code change, or is it still manual?

#AIEvals #RegressionTesting #AIEngineering