Ship an AI-in-the-Loop Suite Through CI — and Tell the Story

The idea, in one line

AI drafts the tests, a human reviews them, and only the approved tests run automatically. The order is the whole point — it's what keeps AI honest.

CI stands for continuous integration: a service that automatically runs your tests every time code changes, so problems show up early.

The pipeline, step by step

1. AI generates candidate tests offline, on your machine.
2. You review them in a pull request — cutting duplicates, fixing weakened checks, and adding the locale and timezone cases AI missed.
3. Only the merged, human-approved suite runs in CI. The AI never approves its own work, and never touches the main branch directly.

See it work

# AI drafts tests, a human reviews the PR, THEN CI runs the suite.
name: qa
on: [pull_request]           # the review gate lives on the PR itself
jobs:
  test:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - uses: actions/setup-python@v5
        with:
          python-version: "3.12"
      - run: pip install -r requirements.txt
      - name: Run the reviewed suite
        run: pytest -q --maxfail=1
      # Tests merge only after a human approves the PR

Read it top to bottom: this runs on every pull request, sets up Python, installs what it needs, and runs the tests. Because it's tied to the pull request, a human still has to approve before anything merges.

Advanced — your two-part interview narrative

This same setup is your interview answer, and the strongest version has two halves. Be specific about both:

• Where you let AI amplify your testing — drafting cases and boilerplate fast.
• Where you refuse to trust it — the checks themselves, security-critical logic, and anything it would merge without a human read.

Grounded in GitHub Actions docs and GitHub's guidance on reviewing AI-generated code