According to @elevenlabsio, AI-powered traffic control enables teams to safely test new software versions by routing a specific share of user traffic to experimental versions while maintaining a stable main version. This approach allows businesses to perform precise A/B tests to determine which AI model or application version delivers superior performance before committing to a full deployment, minimizing risk and optimizing user experience. The method is increasingly adopted in AI-driven SaaS and product development workflows to ensure reliable outcomes and data-driven decision making (source: ElevenLabs @elevenlabsio, Dec 18, 2025).

According to G3mini (@GeminiApp), with the recent releases of Gemini 3 and Nano Banana Pro, the AI community has been actively sharing effective prompting techniques and strategies to optimize AI model outputs. These best practices focus on clarity, specificity, and context, enabling businesses to extract more accurate and actionable insights from generative AI tools. The discussion highlights a growing trend where organizations and developers are investing in prompt engineering to improve AI performance, streamline workflows, and drive innovation in areas such as content generation, customer support automation, and data analysis. By leveraging advanced prompting methods, enterprises can unlock greater value from AI deployments and gain a competitive edge in their industries (source: @GeminiApp, Twitter, Dec 1, 2025).