What is Human-in-the-Loop?
Human-in-the-Loop (HITL) is a design pattern where human judgment is integrated into an AI system's workflow — either for validation, correction, decision-making, or training feedback. The human acts as a quality gate, reviewing AI outputs before they're acted upon or used to improve the model.
Why It Matters
Full AI autonomy is risky for high-stakes decisions — medical diagnoses, financial transactions, content moderation, hiring. HITL is the practical safety pattern that keeps humans in control while leveraging AI's speed and scale. It's also how AI models improve: RLHF (Reinforcement Learning from Human Feedback) is literally humans-in-the-loop improving model behavior.
How It Works
HITL patterns in production:
1. Approval workflow:
- AI generates output (email draft, code change, decision)
- Human reviews and approves/rejects/edits
- Only approved outputs are executed
- Example: AI drafts customer emails, agent reviews before sending
2. Exception handling:
- AI processes routine cases automatically
- Low-confidence or edge cases are escalated to humans
- Human decisions may feed back into training
- Example: AI handles 90% of support tickets; complex ones go to humans