What is Human-in-the-loop?

Human-in-the-loop describes any system where a person reviews, corrects or approves machine output before it counts. In AI training it covers almost all the work: people label data, judge answers and fix mistakes so the model learns from verified examples.

Where the human sits

• Before training: labeling and writing the examples a model learns from.
• During training: rating answers for RLHF and checking synthetic data.
• After release: reviewing flagged outputs and edge cases in production.

Why it is not going away

Each model generation makes simple tasks automatic, but it also raises the bar for what needs checking. The human role moves from labeling cats to judging legal reasoning, which is why demand for expert trainers keeps rising.

Example

A medical transcription model drafts notes from doctor-patient recordings. A human reviewer corrects drug names and dosages before the notes are used, and the corrections are fed back to train the next version.