Designing Commit-to-Lesson Classifiers with Human-in-the-Loop Guardrails

Designing Commit-to-Lesson Classifiers with Human-in-the-Loop Guardrails

Automatically classifying commits into tutorial topics is useful, but fully autonomous labeling drifts over time. You need a mixed system: model suggestions plus reviewer checkpoints.

Step 1: Build a topic schema with confidence bands

{
  "topics": ["ios", "frontend", "python", "security"],
  "auto_publish_confidence": 0.92,
  "review_required_below": 0.92
}

Step 2: Ask model for label + rationale + ambiguity score

def classify(commit_text):
    return {
        "label": "ios",
        "confidence": 0.88,
        "why": "mentions widget timeline + watch sync"
    }