Before it
Harmlessness came from humans labelling large volumes of unpleasant model output.
The problem
That labour is expensive, distressing, and its criteria live in labellers' heads rather than on paper.
What it introduced
Write the principles down, have the model critique and revise its own outputs against them, and train on its own feedback.
Why it matters
It moved part of alignment from tacit labelling to explicit written rules, and made the criteria auditable.
What modern models inherit
AI feedback against stated principles as a supplement to human preference data.
What came next
RLAIF more broadly, and the alignment approach behind Claude.