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A few years ago chatbots were all the rage and consequently Google, Microsoft and many other offered frameworks that can help you build chatbots.
They are all built around setting up a decision tree to steer the conversation to an endpoint and some natural language processing (NLP) capabilities. The decision tree represents the logical flow of the interaction between the user and the bot.
An order-taking chatbot in a pizzeria could, for example, first ask you what kind of pizza you want and present you with the binary choice of vegetarian or non-vegetarian. After your first choice, you’ll be presented with the next step until you finish your order. NLP capabilities can be used to help understand written text input from the user.
Decision tree systems have a long history of being used to methodically come to conclusions in various scenarios, but they are falling out of fashion. Let’s examine the reasons why.
Before diving into why people are abandoning the decision tree approach, let us first look at some cases where it can work:
Now to some of the reasons why the chatbot approach might be falling out of fashion:
Chatbots can be a convenient way to solve very specific problems requiring simple solutions. However, as we’ve seen above, they have their limitations.
In our coming blog post series “The science behind the raffle-lution,” we will discuss the technological advances and research community trends that have allowed raffle.ai to build state-of-the-art AI question-answering systems for customer service, such as our AutoPilot and CoPilot products.
We’ll also discuss the emerging technology that will allow us to develop ever more sophisticated products to tackle challenges that organizations will face in the future.