Generative Answers with Power Virtual Agents provides a useful method for bots to fetch information from an assortment of internal and external sources. It stands out from authored topics as it eliminates the need to establish pre-determined subjects. In essence, generative answers can either be the primary data source of your chatbot or it can serve as a safety net when available topics fail to answer user queries. This functionality simplifies the bot-making procedure by permitting you to create efficient chatbots without necessarily drafting multiple topics to cater to client inquiries.
In the past, if a bot couldn't comprehend a user's objective, it would request for a rephrase of the inquiry. If this failed twice consecutively, the bot would resort to human aid, using the system's Escalate topic. This post illustrates how to configure generative answers to stand as a contingency plan when existing bot topics prove inadequate.
Generative answers play a critical role by acting as a chatbot's chief data repository or a secondary option when bot topics do not satisfy customer queries. They provide a seamless way to enhance the bot's communication efficiency by understanding user intentions without human intervention. They also eliminate the need for pre-created conversation topics, thus saving time in the bot creation process. Essentially, generative answers have revolutionised the ability of automated bots to comprehend and respond to user queries more effectively.
Generative Answers with Power Virtual Agents provide a powerful and convenient way to access and display information from both internal and external sources. Generative answers do not require pre-determined topics to be created before use, making it an ideal solution for quickly building and launching a functional chatbot. Instead of asking the user to rephrase their query if the automated bot doesn't understand, generative answers can be used as a backup option. This article explains how to set up generative answers as part of a chatbot system. It will cover topics such as configuring generative answer settings, configuring external data sources, setting up a fallback topic, and setting up a Generative Answer Model. Additionally, it will discuss best practices for testing and deploying generative answers. Finally, it will explain how to monitor and analyze the performance of a generative answer system.
Microsoft Bot Framework, Artificial Intelligence, Natural Language Processing, Machine Learning, Cloud Computing.