Like a student learning from mistakes, the AI tries different things and gets rewards when it does something correctly.

Learning Through Experience

This method uses human opinions to teach the AI which answers are better, making it sound more natural and helpful.

Adding the Human Touch

Before the human feedback starts, the AI reads billions of sentences to learn how language, facts, and grammar work.

Experts write out perfect answers for the AI to study, giving it a strong starting point for how to follow instructions.

People look at several responses from the AI and rank them from best to worst based on quality and safety.

A separate AI 'judge' is trained to understand the human rankings so it can score the main AI's work automatically.

The AI generates millions of answers and uses the AI judge's scores to figure out how to improve its performance.

To keep the AI from getting confused, a special rule ensures it only changes its behavior slightly at each step.

Safety guardrails prevent the AI from becoming too different from its original training or picking up strange habits.

The goal is to make the AI helpful, honest, and harmless so it is both useful and safe for users.

Humans can teach the AI subtle things that are hard to program, like being polite or understanding a joke.

Special teams try to trick the AI into saying bad things to find and fix safety issues before the public uses it.

Sometimes an AI finds 'loopholes' to get high scores without actually being helpful; researchers work to stop this.

Because people disagree on what 'best' means, the AI must learn from many different viewpoints to be fair.

This process is why modern chatbots feel so much smarter and safer than older AI programs.