In a previous post, Evaluating and Improving Voicebot Flows with Call Stats, we introduced this vital health check for the continuous improvement of voicebot performance. Call statistics gauge bot effectiveness and identify bottlenecks and potential flow disruptions in AI agent workflows. Adding call stats to voicebot flows
At Enterprise Connect 2024, I saw the latest updates around AI in enterprise communications. It was an interesting contrast to my 2023 visit to the same conference. While the hype was more or less the same, there has definitely been progress in turning that hype into reality.
Prompt engineering involves organizing text so that a Generative AI Large Language Model (LLM) can interpret input and generate an expected or desired output. Imagine a friend is making you a sandwich and you want them to prepare it just the way you like it. You say,
Voicebots offer efficient resolution of common customer inquiries, making them an indispensable component of today’s customer service flows. In previous posts, we have seen how you enhance the capabilities of voicebots using the power of LLMs and also integrate such capabilities in a web interface using WebRTC,
