Collaborative training workflows

Collaborative training workflows engage users in providing feedback on AI responses. A common example is comparative feedback, where users choose which of two AI-generated responses they prefer, as shown in ChatGPT's interface. These simple preference selections help create valuable training data for future model versions.

While the AI system doesn't learn immediately from this feedback, companies collect these user evaluations to improve future versions of their models through reinforcement learning from human feedback (RLHF). Over time, as thousands of users provide these preference judgments, the collective feedback helps AI developers understand which responses users find most helpful, accurate, safe, and aligned with human values. This creates a system where user input gradually shapes better AI responses over time, even though individual users may not see immediate improvements based on their specific feedback.