According to God of Prompt (@godofprompt), incorporating relevant background information such as user bios, research data, and previous conversations into AI systems yields significantly better results than relying on clever prompt engineering techniques like 'act as' commands (source: Twitter, 2025-11-16). This trend highlights a shift in AI industry best practices, where organizations can achieve superior AI performance and user alignment by systematically feeding contextual data into large language models. For businesses, this means that investing in robust data integration pipelines and context-aware AI workflows can lead to more accurate, personalized, and commercially valuable AI applications, enhancing customer experience and operational efficiency.

According to Andrej Karpathy (@karpathy), maintaining strong regularization is crucial to prevent model degradation when applying Reinforcement Learning from Human Feedback (RLHF) in AI systems (source: Twitter, June 25, 2025). Karpathy highlights that insufficient regularization during RLHF can lead to 'slop,' where AI models become less precise and reliable. This insight underscores the importance of robust regularization techniques in fine-tuning large language models for enterprise and commercial AI deployments. Businesses leveraging RLHF for AI model improvement should prioritize regularization strategies to ensure model integrity, performance consistency, and trustworthy outputs, directly impacting user satisfaction and operational reliability.