According to @godofprompt on Twitter, implementing structured prompt engineering techniques—such as guiding AI models through planning, execution, and verification steps—dramatically improves output accuracy. Instead of generic prompts like 'do the thing,' providing a scaffolded approach enables AI models to deliver more reliable results. The difference between 70% and 95% accuracy is often attributed to prompt design rather than the underlying model's capabilities (source: @godofprompt, Dec 10, 2025). This insight highlights a major business opportunity: by investing in advanced prompt engineering, enterprises can unlock greater value from existing AI systems without costly model upgrades, directly impacting operational efficiency and competitive advantage.

According to @Smol_AI, OpenRouter has published a groundbreaking empirical study analyzing 100 trillion tokens to present the current state of AI as of December 2025. The study, shared via OpenRouter’s official X account, provides concrete data on large language model (LLM) usage patterns, fine-tuning effectiveness, and scaling laws, which are critical for enterprise AI adoption and optimization strategies. The report highlights emerging business opportunities in AI infrastructure, data curation, and model interoperability, signaling a shift toward more robust, scalable, and efficient AI services for enterprises (source: x.com/OpenRouterAI/status/1996678816820089131; news.smol.ai/issues/25-12-04-openrouter).