Intermediate · Research

Human-in-the-loop

Visual diagram · (in preparation) · Math · (in preparation) · Worked example · 3 difficulty levels.

TL;DR. System designs where humans review, correct, or guide AI outputs as part of the workflow.

Technical Definition

System designs where humans review, correct, or guide AI outputs as part of the workflow.

How it works

HITL improves quality, captures edge cases, and provides ongoing labels for retraining. Common patterns: review queues for low-confidence predictions, RLHF preference labeling, and 'co-pilot' interfaces where humans accept or edit AI suggestions.

Related Concepts

• RLHF (Reinforcement Learning from Human Feedback) — A technique that aligns LLM outputs with human preferences by training a reward model from human comparisons.
• Annotation — The act of attaching labels, tags, or structured information to raw data.
• Active Learning — An ML approach where the model selects which examples it most wants labeled next.