Part 5/12:
Once established, the SFT phase relies on human examples to shape expected outputs, while RLHF provides a mechanism for ongoing improvement by rewarding desirable responses and penalizing unhelpful ones. Through these processes, developers calibrate AI to resonate with users. However, as the deployment of AI accelerates, important questions regarding the variability of cultural responses to AI outputs emerge. These differences in user feedback can significantly affect the AI's learning process and subsequent behavior, leading to results that may not align with intended objectives.