According to @godofprompt, businesses aiming to scale GPT-powered workflows efficiently and cost-effectively should implement three core best practices: resource optimization, automated scaling, and continuous performance monitoring. Optimizing resources involves selecting the right model sizes and hardware to match workload demands, reducing unnecessary compute expenses. Automating scaling ensures that resources are dynamically allocated based on real-time usage, minimizing latency and preventing overprovisioning. Ongoing performance monitoring allows organizations to detect bottlenecks, track cost metrics, and maintain consistent model output quality. These strategies help enterprises leverage generative AI while controlling operational costs and ensuring reliability, as detailed in godofprompt.ai’s comprehensive guide (source: godofprompt.ai/blog/best-practices-for-gpt-workflow-scalability).