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 on Twitter, recent advancements in AI-driven emotional recognition are gaining significant traction, particularly as machine learning models become more adept at interpreting subtle human emotions from text and images (source: twitter.com/karpathy/status/1932857962781114747). This trend is opening up new business opportunities for AI startups and enterprises in customer service, healthcare, and human-computer interaction, where emotional intelligence can enhance user experience and engagement. Companies investing in these technologies are seeing improved sentiment analysis accuracy and more personalized digital interactions, positioning emotional AI as a key growth sector in 2025.