When Your AI Gets Too Popular: Scaling Without Breaking a Sweat (or a Server) cover art

When Your AI Gets Too Popular: Scaling Without Breaking a Sweat (or a Server)

When Your AI Gets Too Popular: Scaling Without Breaking a Sweat (or a Server)

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 In the interesting episode of the Voice First AI podcast, host Michael outlines the challenges and strategies for scaling Conversational AI effectively without compromising the quality or functionality. The episode highlights the importance of starting small, testing in a controlled environment and maintaining a consistent brand voice. Michael also stresses the need to optimize, emphasising the importance of refining conversation flows and adapting to user feedback. Further, this episode cites the crucial role of context management systems, Key Performance Indicators and human-in-the-loop systems for maintaining quality and spotting glitches. Importantly, regular check-ins are essential to prevent any issues from spiralling out of control in automated systems. Lastly, Michael encourages the audience to focus on optimization as the key to long-term success and not just on amplifying output.
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